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#' Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions that are used internally for the STEP calculations.
#'
#' @inheritParams argument_convention
#'
#' @name h_step
#' @include control_step.R
NULL

#' @describeIn h_step creates the windows for STEP, based on the control settings
#'   provided.
#'
#' @param x (`numeric`)\cr biomarker value(s) to use (without `NA`).
#' @param control (named `list`)\cr output from `control_step()`.
#'
#' @return
#' * `h_step_window()` returns a list containing the window-selection matrix `sel`
#'   and the interval information matrix `interval`.
#'
#' @export
h_step_window <- function(x,
                          control = control_step()) {
  checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")

  sel <- matrix(FALSE, length(x), control$num_points)
  out <- matrix(0, control$num_points, 3)
  colnames(out) <- paste("Interval", c("Center", "Lower", "Upper"))
  if (control$use_percentile) {
    # Create windows according to percentile cutoffs.
    out <- cbind(out, out)
    colnames(out)[1:3] <- paste("Percentile", c("Center", "Lower", "Upper"))
    xs <- seq(0, 1, length.out = control$num_points + 2)[-1]
    for (i in seq_len(control$num_points)) {
      out[i, 2:3] <- c(
        max(xs[i] - control$bandwidth, 0),
        min(xs[i] + control$bandwidth, 1)
      )
      out[i, 5:6] <- stats::quantile(x, out[i, 2:3])
      sel[, i] <- x >= out[i, 5] & x <= out[i, 6]
    }
    # Center is the middle point of the percentile window.
    out[, 1] <- xs[-control$num_points - 1]
    out[, 4] <- stats::quantile(x, out[, 1])
  } else {
    # Create windows according to cutoffs.
    m <- c(min(x), max(x))
    xs <- seq(m[1], m[2], length.out = control$num_points + 2)[-1]
    for (i in seq_len(control$num_points)) {
      out[i, 2:3] <- c(
        max(xs[i] - control$bandwidth, m[1]),
        min(xs[i] + control$bandwidth, m[2])
      )
      sel[, i] <- x >= out[i, 2] & x <= out[i, 3]
    }
    # Center is the same as the point for predicting.
    out[, 1] <- xs[-control$num_points - 1]
  }
  list(sel = sel, interval = out)
}

#' @describeIn h_step calculates the estimated treatment effect estimate
#'   on the linear predictor scale and corresponding standard error from a STEP `model` fitted
#'   on `data` given `variables` specification, for a single biomarker value `x`.
#'   This works for both `coxph` and `glm` models, i.e. for calculating log hazard ratio or log odds
#'   ratio estimates.
#'
#' @param model the regression model object.
#'
#' @return
#' * `h_step_trt_effect()` returns a vector with elements `est` and `se`.
#'
#' @export
h_step_trt_effect <- function(data,
                              model,
                              variables,
                              x) {
  checkmate::assert_multi_class(model, c("coxph", "glm"))
  checkmate::assert_number(x)
  assert_df_with_variables(data, variables)
  checkmate::assert_factor(data[[variables$arm]], n.levels = 2)

  newdata <- data[c(1, 1), ]
  newdata[, variables$biomarker] <- x
  newdata[, variables$arm] <- levels(data[[variables$arm]])
  model_terms <- stats::delete.response(stats::terms(model))
  model_frame <- stats::model.frame(model_terms, data = newdata, xlev = model$xlevels)
  mat <- stats::model.matrix(model_terms, data = model_frame, contrasts.arg = model$contrasts)
  coefs <- stats::coef(model)
  # Note: It is important to use the coef subset from matrix, otherwise intercept and
  # strata are included for coxph() models.
  mat <- mat[, names(coefs)]
  mat_diff <- diff(mat)
  est <- mat_diff %*% coefs
  var <- mat_diff %*% stats::vcov(model) %*% t(mat_diff)
  se <- sqrt(var)
  c(
    est = est,
    se = se
  )
}

#' @describeIn h_step builds the model formula used in survival STEP calculations.
#'
#' @return
#' * `h_step_survival_formula()` returns a model formula.
#'
#' @export
h_step_survival_formula <- function(variables,
                                    control = control_step()) {
  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  assert_list_of_variables(variables[c("arm", "biomarker", "event", "time")])
  form <- paste0("Surv(", variables$time, ", ", variables$event, ") ~ ", variables$arm)
  if (control$degree > 0) {
    form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
  }
  if (!is.null(variables$covariates)) {
    form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
  }
  if (!is.null(variables$strata)) {
    form <- paste0(form, " + strata(", paste0(variables$strata, collapse = ", "), ")")
  }
  stats::as.formula(form)
}

#' @describeIn h_step estimates the model with `formula` built based on
#'   `variables` in `data` for a given `subset` and `control` parameters for the
#'   Cox regression.
#'
#' @param formula (`formula`)\cr the regression model formula.
#' @param subset (`logical`)\cr subset vector.
#'
#' @return
#' * `h_step_survival_est()` returns a matrix of number of observations `n`,
#'   `events`, log hazard ratio estimates `loghr`, standard error `se`,
#'   and Wald confidence interval bounds `ci_lower` and `ci_upper`. One row is
#'   included for each biomarker value in `x`.
#'
#' @export
h_step_survival_est <- function(formula,
                                data,
                                variables,
                                x,
                                subset = rep(TRUE, nrow(data)),
                                control = control_coxph()) {
  checkmate::assert_formula(formula)
  assert_df_with_variables(data, variables)
  checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
  checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")

  # Note: `subset` in `coxph` needs to be an expression referring to `data` variables.
  data$.subset <- subset
  coxph_warnings <- NULL
  tryCatch(
    withCallingHandlers(
      expr = {
        fit <- survival::coxph(
          formula = formula,
          data = data,
          subset = .subset,
          ties = control$ties
        )
      },
      warning = function(w) {
        coxph_warnings <<- c(coxph_warnings, w)
        invokeRestart("muffleWarning")
      }
    ),
    finally = {
    }
  )
  if (!is.null(coxph_warnings)) {
    warning(paste(
      "Fit warnings occurred, please consider using a simpler model, or",
      "larger `bandwidth`, less `num_points` in `control_step()` settings"
    ))
  }
  # Produce a matrix with one row per `x` and columns `est` and `se`.
  estimates <- t(vapply(
    X = x,
    FUN = h_step_trt_effect,
    FUN.VALUE = c(1, 2),
    data = data,
    model = fit,
    variables = variables
  ))
  q_norm <- stats::qnorm((1 + control$conf_level) / 2)
  cbind(
    n = fit$n,
    events = fit$nevent,
    loghr = estimates[, "est"],
    se = estimates[, "se"],
    ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
    ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
  )
}

#' @describeIn h_step builds the model formula used in response STEP calculations.
#'
#' @return
#' * `h_step_rsp_formula()` returns a model formula.
#'
#' @export
h_step_rsp_formula <- function(variables,
                               control = c(control_step(), control_logistic())) {
  checkmate::assert_character(variables$covariates, null.ok = TRUE)
  assert_list_of_variables(variables[c("arm", "biomarker", "response")])
  response_definition <- sub(
    pattern = "response",
    replacement = variables$response,
    x = control$response_definition,
    fixed = TRUE
  )
  form <- paste0(response_definition, " ~ ", variables$arm)
  if (control$degree > 0) {
    form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
  }
  if (!is.null(variables$covariates)) {
    form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
  }
  if (!is.null(variables$strata)) {
    strata_arg <- if (length(variables$strata) > 1) {
      paste0("I(interaction(", paste0(variables$strata, collapse = ", "), "))")
    } else {
      variables$strata
    }
    form <- paste0(form, "+ strata(", strata_arg, ")")
  }
  stats::as.formula(form)
}

#' @describeIn h_step estimates the model with `formula` built based on
#'   `variables` in `data` for a given `subset` and `control` parameters for the
#'   logistic regression.
#'
#' @param formula (`formula`)\cr the regression model formula.
#' @param subset (`logical`)\cr subset vector.
#'
#' @return
#' * `h_step_rsp_est()` returns a matrix of number of observations `n`, log odds
#'   ratio estimates `logor`, standard error `se`, and Wald confidence interval bounds
#'   `ci_lower` and `ci_upper`. One row is included for each biomarker value in `x`.
#'
#' @export
h_step_rsp_est <- function(formula,
                           data,
                           variables,
                           x,
                           subset = rep(TRUE, nrow(data)),
                           control = control_logistic()) {
  checkmate::assert_formula(formula)
  assert_df_with_variables(data, variables)
  checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
  checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")
  # Note: `subset` in `glm` needs to be an expression referring to `data` variables.
  data$.subset <- subset
  fit_warnings <- NULL
  tryCatch(
    withCallingHandlers(
      expr = {
        fit <- if (is.null(variables$strata)) {
          stats::glm(
            formula = formula,
            data = data,
            subset = .subset,
            family = stats::binomial("logit")
          )
        } else {
          # clogit needs coxph and strata imported
          survival::clogit(
            formula = formula,
            data = data,
            subset = .subset
          )
        }
      },
      warning = function(w) {
        fit_warnings <<- c(fit_warnings, w)
        invokeRestart("muffleWarning")
      }
    ),
    finally = {
    }
  )
  if (!is.null(fit_warnings)) {
    warning(paste(
      "Fit warnings occurred, please consider using a simpler model, or",
      "larger `bandwidth`, less `num_points` in `control_step()` settings"
    ))
  }
  # Produce a matrix with one row per `x` and columns `est` and `se`.
  estimates <- t(vapply(
    X = x,
    FUN = h_step_trt_effect,
    FUN.VALUE = c(1, 2),
    data = data,
    model = fit,
    variables = variables
  ))
  q_norm <- stats::qnorm((1 + control$conf_level) / 2)
  cbind(
    n = length(fit$y),
    logor = estimates[, "est"],
    se = estimates[, "se"],
    ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
    ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
  )
}

#' Control Function for Descriptive Statistics
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Sets a list of parameters for summaries of descriptive statistics. Typically used internally to specify
#' details for [s_summary()]. This function family is mainly used by [analyze_vars()].
#'
#' @inheritParams argument_convention
#' @param quantiles (`numeric`)\cr of length two to specify the quantiles to calculate.
#' @param quantile_type (`numeric`)\cr between 1 and 9 selecting quantile algorithms to be used.
#'   Default is set to 2 as this matches the default quantile algorithm in SAS `proc univariate` set by `QNTLDEF=5`.
#'   This differs from R's default. See more about `type` in [stats::quantile()].
#' @param test_mean (`numeric`)\cr to test against the mean under the null hypothesis when calculating p-value.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @export
control_analyze_vars <- function(conf_level = 0.95,
                                 quantiles = c(0.25, 0.75),
                                 quantile_type = 2,
                                 test_mean = 0) {
  checkmate::assert_vector(quantiles, len = 2)
  checkmate::assert_int(quantile_type, lower = 1, upper = 9)
  checkmate::assert_numeric(test_mean)
  lapply(quantiles, assert_proportion_value)
  assert_proportion_value(conf_level)
  list(conf_level = conf_level, quantiles = quantiles, quantile_type = quantile_type, test_mean = test_mean)
}

#' Analyze Variables
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [analyze_vars()] generates a summary of one or more variables, using the S3 generic function
#' [s_summary()] to calculate a list of summary statistics. A list of all available statistics for numeric
#' variables can be viewed by running `get_stats("analyze_vars_numeric")` and for non-numeric variables by running
#' `get_stats("analyze_vars_counts")`. Use the `.stats` parameter to specify the statistics to include in your output
#' summary table.
#'
#' @details
#' **Automatic digit formatting:** The number of digits to display can be automatically determined from the analyzed
#' variable(s) (`vars`) for certain statistics by setting the statistic format to `"auto"` in `.formats`.
#' This utilizes the [format_auto()] formatting function. Note that only data for the current row & variable (for all
#' columns) will be considered (`.df_row[[.var]]`, see [`rtables::additional_fun_params`]) and not the whole dataset.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("analyze_vars_numeric")` to see
#'   statistics available for numeric variables, and `get_stats("analyze_vars_counts")` for statistics available
#'   for non-numeric variables.
#'
#' @name analyze_variables
#' @order 1
NULL

#' @describeIn analyze_variables S3 generic function to produces a variable summary.
#'
#' @return
#' * `s_summary()` returns different statistics depending on the class of `x`.
#'
#' @export
s_summary <- function(x,
                      na.rm = TRUE, # nolint
                      denom,
                      .N_row, # nolint
                      .N_col, # nolint
                      .var,
                      ...) {
  checkmate::assert_flag(na.rm)
  UseMethod("s_summary", x)
}

#' @describeIn analyze_variables Method for `numeric` class.
#'
#' @param control (`list`)\cr parameters for descriptive statistics details, specified by using
#'   the helper function [control_analyze_vars()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for mean and median.
#'   * `quantiles` (`numeric`)\cr vector of length two to specify the quantiles.
#'   * `quantile_type` (`numeric`)\cr between 1 and 9 selecting quantile algorithms to be used.
#'     See more about `type` in [stats::quantile()].
#'   * `test_mean` (`numeric`)\cr value to test against the mean under the null hypothesis when calculating p-value.
#'
#' @return
#'   * If `x` is of class `numeric`, returns a `list` with the following named `numeric` items:
#'     * `n`: The [length()] of `x`.
#'     * `sum`: The [sum()] of `x`.
#'     * `mean`: The [mean()] of `x`.
#'     * `sd`: The [stats::sd()] of `x`.
#'     * `se`: The standard error of `x` mean, i.e.: (`sd(x) / sqrt(length(x))`).
#'     * `mean_sd`: The [mean()] and [stats::sd()] of `x`.
#'     * `mean_se`: The [mean()] of `x` and its standard error (see above).
#'     * `mean_ci`: The CI for the mean of `x` (from [stat_mean_ci()]).
#'     * `mean_sei`: The SE interval for the mean of `x`, i.e.: ([mean()] -/+ [stats::sd()] / [sqrt()]).
#'     * `mean_sdi`: The SD interval for the mean of `x`, i.e.: ([mean()] -/+ [stats::sd()]).
#'     * `mean_pval`: The two-sided p-value of the mean of `x` (from [stat_mean_pval()]).
#'     * `median`: The [stats::median()] of `x`.
#'     * `mad`: The median absolute deviation of `x`, i.e.: ([stats::median()] of `xc`,
#'       where `xc` = `x` - [stats::median()]).
#'     * `median_ci`: The CI for the median of `x` (from [stat_median_ci()]).
#'     * `quantiles`: Two sample quantiles of `x` (from [stats::quantile()]).
#'     * `iqr`: The [stats::IQR()] of `x`.
#'     * `range`: The [range_noinf()] of `x`.
#'     * `min`: The [max()] of `x`.
#'     * `max`: The [min()] of `x`.
#'     * `median_range`: The [median()] and [range_noinf()] of `x`.
#'     * `cv`: The coefficient of variation of `x`, i.e.: ([stats::sd()] / [mean()] * 100).
#'     * `geom_mean`: The geometric mean of `x`, i.e.: (`exp(mean(log(x)))`).
#'     * `geom_cv`: The geometric coefficient of variation of `x`, i.e.: (`sqrt(exp(sd(log(x)) ^ 2) - 1) * 100`).
#'
#' @note
#' * If `x` is an empty vector, `NA` is returned. This is the expected feature so as to return `rcell` content in
#'   `rtables` when the intersection of a column and a row delimits an empty data selection.
#' * When the `mean` function is applied to an empty vector, `NA` will be returned instead of `NaN`, the latter
#'   being standard behavior in R.
#'
#' @method s_summary numeric
#'
#' @examples
#' # `s_summary.numeric`
#'
#' ## Basic usage: empty numeric returns NA-filled items.
#' s_summary(numeric())
#'
#' ## Management of NA values.
#' x <- c(NA_real_, 1)
#' s_summary(x, na.rm = TRUE)
#' s_summary(x, na.rm = FALSE)
#'
#' x <- c(NA_real_, 1, 2)
#' s_summary(x, stats = NULL)
#'
#' ## Benefits in `rtables` contructions:
#' dta_test <- data.frame(
#'   Group = rep(LETTERS[1:3], each = 2),
#'   sub_group = rep(letters[1:2], each = 3),
#'   x = 1:6
#' )
#'
#' ## The summary obtained in with `rtables`:
#' basic_table() %>%
#'   split_cols_by(var = "Group") %>%
#'   split_rows_by(var = "sub_group") %>%
#'   analyze(vars = "x", afun = s_summary) %>%
#'   build_table(df = dta_test)
#'
#' ## By comparison with `lapply`:
#' X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group)))
#' lapply(X, function(x) s_summary(x$x))
#'
#' @export
s_summary.numeric <- function(x,
                              na.rm = TRUE, # nolint
                              denom,
                              .N_row, # nolint
                              .N_col, # nolint
                              .var,
                              control = control_analyze_vars(),
                              ...) {
  checkmate::assert_numeric(x)

  if (na.rm) {
    x <- x[!is.na(x)]
  }

  y <- list()

  y$n <- c("n" = length(x))

  y$sum <- c("sum" = ifelse(length(x) == 0, NA_real_, sum(x, na.rm = FALSE)))

  y$mean <- c("mean" = ifelse(length(x) == 0, NA_real_, mean(x, na.rm = FALSE)))

  y$sd <- c("sd" = stats::sd(x, na.rm = FALSE))

  y$se <- c("se" = stats::sd(x, na.rm = FALSE) / sqrt(length(stats::na.omit(x))))

  y$mean_sd <- c(y$mean, "sd" = stats::sd(x, na.rm = FALSE))

  y$mean_se <- c(y$mean, y$se)

  mean_ci <- stat_mean_ci(x, conf_level = control$conf_level, na.rm = FALSE, gg_helper = FALSE)
  y$mean_ci <- formatters::with_label(mean_ci, paste("Mean", f_conf_level(control$conf_level)))

  mean_sei <- y$mean[[1]] + c(-1, 1) * stats::sd(x, na.rm = FALSE) / sqrt(y$n)
  names(mean_sei) <- c("mean_sei_lwr", "mean_sei_upr")
  y$mean_sei <- formatters::with_label(mean_sei, "Mean -/+ 1xSE")

  mean_sdi <- y$mean[[1]] + c(-1, 1) * stats::sd(x, na.rm = FALSE)
  names(mean_sdi) <- c("mean_sdi_lwr", "mean_sdi_upr")
  y$mean_sdi <- formatters::with_label(mean_sdi, "Mean -/+ 1xSD")

  mean_pval <- stat_mean_pval(x, test_mean = control$test_mean, na.rm = FALSE, n_min = 2)
  y$mean_pval <- formatters::with_label(mean_pval, paste("Mean", f_pval(control$test_mean)))

  y$median <- c("median" = stats::median(x, na.rm = FALSE))

  y$mad <- c("mad" = stats::median(x - y$median, na.rm = FALSE))

  median_ci <- stat_median_ci(x, conf_level = control$conf_level, na.rm = FALSE, gg_helper = FALSE)
  y$median_ci <- formatters::with_label(median_ci, paste("Median", f_conf_level(control$conf_level)))

  q <- control$quantiles
  if (any(is.na(x))) {
    qnts <- rep(NA_real_, length(q))
  } else {
    qnts <- stats::quantile(x, probs = q, type = control$quantile_type, na.rm = FALSE)
  }
  names(qnts) <- paste("quantile", q, sep = "_")
  y$quantiles <- formatters::with_label(qnts, paste0(paste(paste0(q * 100, "%"), collapse = " and "), "-ile"))

  y$iqr <- c("iqr" = ifelse(
    any(is.na(x)),
    NA_real_,
    stats::IQR(x, na.rm = FALSE, type = control$quantile_type)
  ))

  y$range <- stats::setNames(range_noinf(x, na.rm = FALSE), c("min", "max"))
  y$min <- y$range[1]
  y$max <- y$range[2]

  y$median_range <- formatters::with_label(c(y$median, y$range), "Median (Min - Max)")

  y$cv <- c("cv" = unname(y$sd) / unname(y$mean) * 100)

  # Convert negative values to NA for log calculation.
  x_no_negative_vals <- x
  x_no_negative_vals[x_no_negative_vals <= 0] <- NA
  y$geom_mean <- c("geom_mean" = exp(mean(log(x_no_negative_vals), na.rm = FALSE)))
  geom_mean_ci <- stat_mean_ci(x, conf_level = control$conf_level, na.rm = FALSE, gg_helper = FALSE, geom_mean = TRUE)
  y$geom_mean_ci <- formatters::with_label(geom_mean_ci, paste("Geometric Mean", f_conf_level(control$conf_level)))

  y$geom_cv <- c("geom_cv" = sqrt(exp(stats::sd(log(x_no_negative_vals), na.rm = FALSE) ^ 2) - 1) * 100) # styler: off

  y
}

#' @describeIn analyze_variables Method for `factor` class.
#'
#' @param denom (`string`)\cr choice of denominator for factor proportions. Options are:
#'   * `n`: number of values in this row and column intersection.
#'   * `N_row`: total number of values in this row across columns.
#'   * `N_col`: total number of values in this column across rows.
#'
#' @return
#'   * If `x` is of class `factor` or converted from `character`, returns a `list` with named `numeric` items:
#'     * `n`: The [length()] of `x`.
#'     * `count`: A list with the number of cases for each level of the factor `x`.
#'     * `count_fraction`: Similar to `count` but also includes the proportion of cases for each level of the
#'       factor `x` relative to the denominator, or `NA` if the denominator is zero.
#'
#' @note
#' * If `x` is an empty `factor`, a list is still returned for `counts` with one element
#'   per factor level. If there are no levels in `x`, the function fails.
#' * If factor variables contain `NA`, these `NA` values are excluded by default. To include `NA` values
#'   set `na.rm = FALSE` and missing values will be displayed as an `NA` level. Alternatively, an explicit
#'   factor level can be defined for `NA` values during pre-processing via [df_explicit_na()] - the
#'   default `na_level` (`"<Missing>"`) will also be excluded when `na.rm` is set to `TRUE`.
#'
#' @method s_summary factor
#'
#' @examples
#' # `s_summary.factor`
#'
#' ## Basic usage:
#' s_summary(factor(c("a", "a", "b", "c", "a")))
#'
#' # Empty factor returns zero-filled items.
#' s_summary(factor(levels = c("a", "b", "c")))
#'
#' ## Management of NA values.
#' x <- factor(c(NA, "Female"))
#' x <- explicit_na(x)
#' s_summary(x, na.rm = TRUE)
#' s_summary(x, na.rm = FALSE)
#'
#' ## Different denominators.
#' x <- factor(c("a", "a", "b", "c", "a"))
#' s_summary(x, denom = "N_row", .N_row = 10L)
#' s_summary(x, denom = "N_col", .N_col = 20L)
#'
#' @export
s_summary.factor <- function(x,
                             na.rm = TRUE, # nolint
                             denom = c("n", "N_row", "N_col"),
                             .N_row, # nolint
                             .N_col, # nolint
                             ...) {
  assert_valid_factor(x)
  denom <- match.arg(denom)

  if (na.rm) {
    x <- x[!is.na(x)] %>% fct_discard("<Missing>")
  } else {
    x <- x %>% explicit_na(label = "NA")
  }

  y <- list()

  y$n <- length(x)

  y$count <- as.list(table(x, useNA = "ifany"))
  dn <- switch(denom,
    n = length(x),
    N_row = .N_row,
    N_col = .N_col
  )
  y$count_fraction <- lapply(
    y$count,
    function(x) {
      c(x, ifelse(dn > 0, x / dn, 0))
    }
  )

  y$n_blq <- sum(grepl("BLQ|LTR|<[1-9]|<PCLLOQ", x))

  y
}

#' @describeIn analyze_variables Method for `character` class. This makes an automatic
#'   conversion to factor (with a warning) and then forwards to the method for factors.
#'
#' @param verbose (`logical`)\cr Defaults to `TRUE`, which prints out warnings and messages. It is mainly used
#'   to print out information about factor casting.
#'
#' @note
#' * Automatic conversion of character to factor does not guarantee that the table
#'   can be generated correctly. In particular for sparse tables this very likely can fail.
#'   It is therefore better to always pre-process the dataset such that factors are manually
#'   created from character variables before passing the dataset to [rtables::build_table()].
#'
#' @method s_summary character
#'
#' @examples
#' # `s_summary.character`
#'
#' ## Basic usage:
#' s_summary(c("a", "a", "b", "c", "a"), .var = "x", verbose = FALSE)
#' s_summary(c("a", "a", "b", "c", "a", ""), .var = "x", na.rm = FALSE, verbose = FALSE)
#'
#' @export
s_summary.character <- function(x,
                                na.rm = TRUE, # nolint
                                denom = c("n", "N_row", "N_col"),
                                .N_row, # nolint
                                .N_col, # nolint
                                .var,
                                verbose = TRUE,
                                ...) {
  if (na.rm) {
    y <- as_factor_keep_attributes(x, verbose = verbose)
  } else {
    y <- as_factor_keep_attributes(x, verbose = verbose, na_level = "NA")
  }

  s_summary(
    x = y,
    na.rm = na.rm,
    denom = denom,
    .N_row = .N_row,
    .N_col = .N_col,
    ...
  )
}

#' @describeIn analyze_variables Method for `logical` class.
#'
#' @param denom (`string`)\cr choice of denominator for proportion. Options are:
#'   * `n`: number of values in this row and column intersection.
#'   * `N_row`: total number of values in this row across columns.
#'   * `N_col`: total number of values in this column across rows.
#'
#' @return
#'   * If `x` is of class `logical`, returns a `list` with named `numeric` items:
#'     * `n`: The [length()] of `x` (possibly after removing `NA`s).
#'     * `count`: Count of `TRUE` in `x`.
#'     * `count_fraction`: Count and proportion of `TRUE` in `x` relative to the denominator, or `NA` if the
#'       denominator is zero. Note that `NA`s in `x` are never counted or leading to `NA` here.
#'
#' @method s_summary logical
#'
#' @examples
#' # `s_summary.logical`
#'
#' ## Basic usage:
#' s_summary(c(TRUE, FALSE, TRUE, TRUE))
#'
#' # Empty factor returns zero-filled items.
#' s_summary(as.logical(c()))
#'
#' ## Management of NA values.
#' x <- c(NA, TRUE, FALSE)
#' s_summary(x, na.rm = TRUE)
#' s_summary(x, na.rm = FALSE)
#'
#' ## Different denominators.
#' x <- c(TRUE, FALSE, TRUE, TRUE)
#' s_summary(x, denom = "N_row", .N_row = 10L)
#' s_summary(x, denom = "N_col", .N_col = 20L)
#'
#' @export
s_summary.logical <- function(x,
                              na.rm = TRUE, # nolint
                              denom = c("n", "N_row", "N_col"),
                              .N_row, # nolint
                              .N_col, # nolint
                              ...) {
  denom <- match.arg(denom)
  if (na.rm) x <- x[!is.na(x)]
  y <- list()
  y$n <- length(x)
  count <- sum(x, na.rm = TRUE)
  dn <- switch(denom,
    n = length(x),
    N_row = .N_row,
    N_col = .N_col
  )
  y$count <- count
  y$count_fraction <- c(count, ifelse(dn > 0, count / dn, 0))
  y$n_blq <- 0L
  y
}

#' @describeIn analyze_variables Formatted analysis function which is used as `afun` in `analyze_vars()` and
#'   `compare_vars()` and as `cfun` in `summarize_colvars()`.
#'
#' @param compare (`logical`)\cr Whether comparison statistics should be analyzed instead of summary statistics
#'   (`compare = TRUE` adds `pval` statistic comparing against reference group).
#'
#' @return
#' * `a_summary()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @note
#' * To use for comparison (with additional p-value statistic), parameter `compare` must be set to `TRUE`.
#' * Ensure that either all `NA` values are converted to an explicit `NA` level or all `NA` values are left as is.
#'
#' @examples
#' a_summary(factor(c("a", "a", "b", "c", "a")), .N_row = 10, .N_col = 10)
#' a_summary(
#'   factor(c("a", "a", "b", "c", "a")),
#'   .ref_group = factor(c("a", "a", "b", "c")), compare = TRUE
#' )
#'
#' a_summary(c("A", "B", "A", "C"), .var = "x", .N_col = 10, .N_row = 10, verbose = FALSE)
#' a_summary(
#'   c("A", "B", "A", "C"),
#'   .ref_group = c("B", "A", "C"), .var = "x", compare = TRUE, verbose = FALSE
#' )
#'
#' a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10)
#' a_summary(
#'   c(TRUE, FALSE, FALSE, TRUE, TRUE),
#'   .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare = TRUE
#' )
#'
#' a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = "bla")
#' a_summary(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .var = "bla", compare = TRUE)
#'
#' @export
a_summary <- function(x,
                      .N_col, # nolint
                      .N_row, # nolint
                      .var = NULL,
                      .df_row = NULL,
                      .ref_group = NULL,
                      .in_ref_col = FALSE,
                      compare = FALSE,
                      .stats = NULL,
                      .formats = NULL,
                      .labels = NULL,
                      .indent_mods = NULL,
                      na.rm = TRUE, # nolint
                      na_str = default_na_str(),
                      ...) {
  extra_args <- list(...)
  if (is.numeric(x)) {
    type <- "numeric"
    if (!is.null(.stats) && any(grepl("^pval", .stats))) {
      .stats[grepl("^pval", .stats)] <- "pval" # tmp fix xxx
    }
  } else {
    type <- "counts"
    if (!is.null(.stats) && any(grepl("^pval", .stats))) {
      .stats[grepl("^pval", .stats)] <- "pval_counts" # tmp fix xxx
    }
  }

  # If one col has NA vals, must add NA row to other cols (using placeholder lvl `fill-na-level`)
  if (any(is.na(.df_row[[.var]])) && !any(is.na(x)) && !na.rm) levels(x) <- c(levels(x), "fill-na-level")

  x_stats <- if (!compare) {
    s_summary(x = x, .N_col = .N_col, .N_row = .N_row, na.rm = na.rm, ...)
  } else {
    s_compare(
      x = x, .N_col = .N_col, .N_row = .N_row, na.rm = na.rm, .ref_group = .ref_group, .in_ref_col = .in_ref_col, ...
    )
  }

  # Fill in with formatting defaults if needed
  met_grp <- paste0(c("analyze_vars", type), collapse = "_")
  .stats <- get_stats(met_grp, stats_in = .stats, add_pval = compare)
  .formats <- get_formats_from_stats(.stats, .formats)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods)

  lbls <- get_labels_from_stats(.stats, .labels)
  # Check for custom labels from control_analyze_vars
  .labels <- if ("control" %in% names(extra_args)) {
    lbls %>% labels_use_control(extra_args[["control"]], .labels)
  } else {
    lbls
  }

  if ("count_fraction_fixed_dp" %in% .stats) x_stats[["count_fraction_fixed_dp"]] <- x_stats[["count_fraction"]]
  x_stats <- x_stats[.stats]

  if (is.factor(x) || is.character(x)) {
    # Ungroup statistics with values for each level of x
    x_ungrp <- ungroup_stats(x_stats, .formats, .labels, .indent_mods)
    x_stats <- x_ungrp[["x"]]
    .formats <- x_ungrp[[".formats"]]
    .labels <- gsub("fill-na-level", "NA", x_ungrp[[".labels"]])
    .indent_mods <- x_ungrp[[".indent_mods"]]
  }

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = .labels,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .format_na_strs = na_str
  )
}

#' @describeIn analyze_variables Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @param ... arguments passed to `s_summary()`.
#' @param .indent_mods (named `vector` of `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @return
#' * `analyze_vars()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_summary()` to the table layout.
#'
#' @examples
#' ## Fabricated dataset.
#' dta_test <- data.frame(
#'   USUBJID = rep(1:6, each = 3),
#'   PARAMCD = rep("lab", 6 * 3),
#'   AVISIT  = rep(paste0("V", 1:3), 6),
#'   ARM     = rep(LETTERS[1:3], rep(6, 3)),
#'   AVAL    = c(9:1, rep(NA, 9))
#' )
#'
#' # `analyze_vars()` in `rtables` pipelines
#' ## Default output within a `rtables` pipeline.
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(vars = "AVAL")
#'
#' build_table(l, df = dta_test)
#'
#' ## Select and format statistics output.
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(
#'     vars = "AVAL",
#'     .stats = c("n", "mean_sd", "quantiles"),
#'     .formats = c("mean_sd" = "xx.x, xx.x"),
#'     .labels = c(n = "n", mean_sd = "Mean, SD", quantiles = c("Q1 - Q3"))
#'   )
#'
#' build_table(l, df = dta_test)
#'
#' ## Use arguments interpreted by `s_summary`.
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(vars = "AVAL", na.rm = FALSE)
#'
#' build_table(l, df = dta_test)
#'
#' ## Handle `NA` levels first when summarizing factors.
#' dta_test$AVISIT <- NA_character_
#' dta_test <- df_explicit_na(dta_test)
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   analyze_vars(vars = "AVISIT", na.rm = FALSE)
#'
#' build_table(l, df = dta_test)
#'
#' # auto format
#' dt <- data.frame("VAR" = c(0.001, 0.2, 0.0011000, 3, 4))
#' basic_table() %>%
#'   analyze_vars(
#'     vars = "VAR",
#'     .stats = c("n", "mean", "mean_sd", "range"),
#'     .formats = c("mean_sd" = "auto", "range" = "auto")
#'   ) %>%
#'   build_table(dt)
#'
#' @export
#' @order 2
analyze_vars <- function(lyt,
                         vars,
                         var_labels = vars,
                         na_str = default_na_str(),
                         nested = TRUE,
                         ...,
                         na.rm = TRUE, # nolint
                         show_labels = "default",
                         table_names = vars,
                         section_div = NA_character_,
                         .stats = c("n", "mean_sd", "median", "range", "count_fraction"),
                         .formats = NULL,
                         .labels = NULL,
                         .indent_mods = NULL) {
  extra_args <- list(.stats = .stats, na.rm = na.rm, na_str = na_str, ...)
  if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
  if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
  if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods

  analyze(
    lyt = lyt,
    vars = vars,
    var_labels = var_labels,
    afun = a_summary,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    inclNAs = TRUE,
    show_labels = show_labels,
    table_names = table_names,
    section_div = section_div
  )
}

#' Create a forest plot from an `rtable`
#'
#' Given a [rtables::rtable()] object with at least one column with a single value and one column with 2
#' values, converts table to a [ggplot2::ggplot()] object and generates an accompanying forest plot. The
#' table and forest plot are printed side-by-side.
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams rtable2gg
#' @inheritParams argument_convention
#' @param tbl (`rtable`)\cr table with at least one column with a single value and one column with 2 values.
#' @param col_x (`integer`)\cr column index with estimator. By default tries to get this from
#'   `tbl` attribute `col_x`, otherwise needs to be manually specified. If `NULL`, points will be excluded
#'   from forest plot.
#' @param col_ci (`integer`)\cr column index with confidence intervals. By default tries to get this from
#'   `tbl` attribute `col_ci`, otherwise needs to be manually specified. If `NULL`, lines will be excluded
#'   from forest plot.
#' @param vline (`numeric`)\cr x coordinate for vertical line, if `NULL` then the line is omitted.
#' @param forest_header (`character`, length 2)\cr text displayed to the left and right of `vline`, respectively.
#'   If `vline = NULL` then `forest_header` is not printed. By default tries to get this from `tbl` attribute
#'   `forest_header`. If `NULL`, defaults will be extracted from the table if possible, and set to
#'   `"Comparison\nBetter"` and `"Treatment\nBetter"` if not.
#' @param xlim (`numeric`)\cr limits for x axis.
#' @param logx (`flag`)\cr show the x-values on logarithm scale.
#' @param x_at (`numeric`)\cr x-tick locations, if `NULL`, `x_at` is set to `vline` and both `xlim` values.
#' @param width_row_names `r lifecycle::badge("deprecated")` Please use the `lbl_col_padding` argument instead.
#' @param width_columns (`vector` of `numeric`)\cr a vector of column widths. Each element's position in
#'   `colwidths` corresponds to the column of `tbl` in the same position. If `NULL`, column widths are calculated
#'   according to maximum number of characters per column.
#' @param width_forest `r lifecycle::badge("deprecated")` Please use the `rel_width_forest` argument instead.
#' @param rel_width_forest (`proportion`)\cr proportion of total width to allocate to the forest plot. Relative
#'   width of table is then `1 - rel_width_forest`. If `as_list = TRUE`, this parameter is ignored.
#' @param font_size (`numeric`)\cr font size.
#' @param col_symbol_size (`integer`)\cr column index from `tbl` containing data to be used
#'   to determine relative size for estimator plot symbol. Typically, the symbol size is proportional
#'   to the sample size used to calculate the estimator. If `NULL`, the same symbol size is used for all subgroups.
#'   By default tries to get this from `tbl` attribute `col_symbol_size`, otherwise needs to be manually specified.
#' @param col (`character`)\cr color(s).
#' @param ggtheme (`theme`)\cr a graphical theme as provided by `ggplot2` to control styling of the plot.
#' @param as_list (`flag`)\cr whether the two `ggplot` objects should be returned as a list. If `TRUE`, a named list
#'   with two elements, `table` and `plot`, will be returned. If `FALSE` (default) the table and forest plot are
#'   printed side-by-side via [cowplot::plot_grid()].
#' @param gp `r lifecycle::badge("deprecated")` `g_forest` is now generated as a `ggplot` object. This argument
#'   is no longer used.
#' @param draw `r lifecycle::badge("deprecated")` `g_forest` is now generated as a `ggplot` object. This argument
#'   is no longer used.
#' @param newpage `r lifecycle::badge("deprecated")` `g_forest` is now generated as a `ggplot` object. This argument
#'   is no longer used.
#'
#' @return `ggplot` forest plot and table.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#' library(nestcolor)
#'
#' adrs <- tern_ex_adrs
#' n_records <- 20
#' adrs_labels <- formatters::var_labels(adrs, fill = TRUE)
#' adrs <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
#'   slice(seq_len(n_records)) %>%
#'   droplevels() %>%
#'   mutate(
#'     # Reorder levels of factor to make the placebo group the reference arm.
#'     ARM = fct_relevel(ARM, "B: Placebo"),
#'     rsp = AVALC == "CR"
#'   )
#' formatters::var_labels(adrs) <- c(adrs_labels, "Response")
#' df <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "STRATA2")),
#'   data = adrs
#' )
#' # Full commonly used response table.
#'
#' tbl <- basic_table() %>%
#'   tabulate_rsp_subgroups(df)
#' g_forest(tbl)
#'
#' # Odds ratio only table.
#'
#' tbl_or <- basic_table() %>%
#'   tabulate_rsp_subgroups(df, vars = c("n_tot", "or", "ci"))
#' g_forest(
#'   tbl_or,
#'   forest_header = c("Comparison\nBetter", "Treatment\nBetter")
#' )
#'
#' # Survival forest plot example.
#' adtte <- tern_ex_adtte
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte, fill = TRUE)
#' adtte_f <- adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X"),
#'     SEX %in% c("M", "F")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
#'     SEX = droplevels(SEX),
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- list(
#'   "ARM" = adtte_labels["ARM"],
#'   "SEX" = adtte_labels["SEX"],
#'   "AVALU" = adtte_labels["AVALU"],
#'   "is_event" = "Event Flag"
#' )
#' formatters::var_labels(adtte_f)[names(labels)] <- as.character(labels)
#' df <- extract_survival_subgroups(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM", subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f
#' )
#' table_hr <- basic_table() %>%
#'   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
#' g_forest(table_hr)
#'
#' # Works with any `rtable`.
#' tbl <- rtable(
#'   header = c("E", "CI", "N"),
#'   rrow("", 1, c(.8, 1.2), 200),
#'   rrow("", 1.2, c(1.1, 1.4), 50)
#' )
#' g_forest(
#'   tbl = tbl,
#'   col_x = 1,
#'   col_ci = 2,
#'   xlim = c(0.5, 2),
#'   x_at = c(0.5, 1, 2),
#'   col_symbol_size = 3
#' )
#'
#' tbl <- rtable(
#'   header = rheader(
#'     rrow("", rcell("A", colspan = 2)),
#'     rrow("", "c1", "c2")
#'   ),
#'   rrow("row 1", 1, c(.8, 1.2)),
#'   rrow("row 2", 1.2, c(1.1, 1.4))
#' )
#' g_forest(
#'   tbl = tbl,
#'   col_x = 1,
#'   col_ci = 2,
#'   xlim = c(0.5, 2),
#'   x_at = c(0.5, 1, 2),
#'   vline = 1,
#'   forest_header = c("Hello", "World")
#' )
#'
#' @export
g_forest <- function(tbl,
                     col_x = attr(tbl, "col_x"),
                     col_ci = attr(tbl, "col_ci"),
                     vline = 1,
                     forest_header = attr(tbl, "forest_header"),
                     xlim = c(0.1, 10),
                     logx = TRUE,
                     x_at = c(0.1, 1, 10),
                     width_row_names = lifecycle::deprecated(),
                     width_columns = NULL,
                     width_forest = lifecycle::deprecated(),
                     lbl_col_padding = 0,
                     rel_width_forest = 0.25,
                     font_size = 12,
                     col_symbol_size = attr(tbl, "col_symbol_size"),
                     col = getOption("ggplot2.discrete.colour")[1],
                     ggtheme = NULL,
                     as_list = FALSE,
                     gp = lifecycle::deprecated(),
                     draw = lifecycle::deprecated(),
                     newpage = lifecycle::deprecated()) {
  # Deprecated argument warnings
  if (lifecycle::is_present(width_row_names)) {
    lifecycle::deprecate_warn(
      "0.9.3", "g_forest(width_row_names)", "g_forest(lbl_col_padding)",
      details = "The width of the row label column can be adjusted via the `lbl_col_padding` parameter."
    )
  }
  if (lifecycle::is_present(width_forest)) {
    lifecycle::deprecate_warn(
      "0.9.3", "g_forest(width_forest)", "g_forest(rel_width_forest)",
      details = "Relative width of the forest plot (as a proportion) can be set via the `rel_width_forest` parameter."
    )
  }
  if (lifecycle::is_present(gp)) {
    lifecycle::deprecate_warn(
      "0.9.3", "g_forest(gp)", "g_forest(ggtheme)",
      details = paste(
        "`g_forest` is now generated as a `ggplot` object.",
        "Additional display settings should be supplied via the `ggtheme` parameter."
      )
    )
  }
  if (lifecycle::is_present(draw)) {
    lifecycle::deprecate_warn(
      "0.9.3", "g_forest(draw)",
      details = "`g_forest` now generates `ggplot` objects. This parameter has no effect."
    )
  }
  if (lifecycle::is_present(newpage)) {
    lifecycle::deprecate_warn(
      "0.9.3", "g_forest(newpage)",
      details = "`g_forest` now generates `ggplot` objects. This parameter has no effect."
    )
  }

  checkmate::assert_class(tbl, "VTableTree")
  checkmate::assert_number(col_x, lower = 0, upper = ncol(tbl), null.ok = TRUE)
  checkmate::assert_number(col_ci, lower = 0, upper = ncol(tbl), null.ok = TRUE)
  checkmate::assert_number(col_symbol_size, lower = 0, upper = ncol(tbl), null.ok = TRUE)
  checkmate::assert_number(font_size, lower = 0)
  checkmate::assert_character(col, null.ok = TRUE)
  checkmate::assert_true(is.null(col) | length(col) == 1 | length(col) == nrow(tbl))

  # Extract info from table
  mat <- matrix_form(tbl)
  mat_strings <- formatters::mf_strings(mat)
  nlines_hdr <- formatters::mf_nlheader(mat)
  nrows_body <- nrow(mat_strings) - nlines_hdr
  tbl_stats <- mat_strings[nlines_hdr, -1]

  # Generate and modify table as ggplot object
  gg_table <- rtable2gg(tbl, fontsize = font_size, colwidths = width_columns, lbl_col_padding = lbl_col_padding) +
    theme(plot.margin = margin(0, 0, 0, 0.025, "npc"))
  gg_table$scales$scales[[1]]$expand <- c(0.01, 0.01)
  gg_table$scales$scales[[2]]$limits[2] <- nrow(mat_strings) + 1
  if (nlines_hdr == 2) {
    gg_table$scales$scales[[2]]$expand <- c(0, 0)
    arms <- unique(mat_strings[1, ][nzchar(trimws(mat_strings[1, ]))])
  } else {
    arms <- NULL
  }

  tbl_df <- as_result_df(tbl)
  dat_cols <- seq(which(names(tbl_df) == "node_class") + 1, ncol(tbl_df))
  tbl_df <- tbl_df[, c(which(names(tbl_df) == "row_num"), dat_cols)]
  names(tbl_df) <- c("row_num", tbl_stats)

  # Check table data columns
  if (!is.null(col_ci)) {
    ci_col <- col_ci + 1
  } else {
    tbl_df[["empty_ci"]] <- rep(list(c(NA_real_, NA_real_)), nrow(tbl_df))
    ci_col <- which(names(tbl_df) == "empty_ci")
  }
  if (length(tbl_df[, ci_col][[1]]) != 2) stop("CI column must have two elements (lower and upper limits).")

  if (!is.null(col_x)) {
    x_col <- col_x + 1
  } else {
    tbl_df[["empty_x"]] <- NA_real_
    x_col <- which(names(tbl_df) == "empty_x")
  }
  if (!is.null(col_symbol_size)) {
    sym_size <- unlist(tbl_df[, col_symbol_size + 1])
  } else {
    sym_size <- rep(1, nrow(tbl_df))
  }

  tbl_df[, c("ci_lwr", "ci_upr")] <- t(sapply(tbl_df[, ci_col], unlist))
  x <- unlist(tbl_df[, x_col])
  lwr <- unlist(tbl_df[["ci_lwr"]])
  upr <- unlist(tbl_df[["ci_upr"]])
  row_num <- nrow(mat_strings) - tbl_df[["row_num"]] - as.numeric(nlines_hdr == 2)

  if (is.null(col)) col <- "#343cff"
  if (length(col) == 1) col <- rep(col, nrow(tbl_df))
  if (is.null(x_at)) x_at <- union(xlim, vline)
  x_labels <- x_at

  # Apply log transformation
  if (logx) {
    x_t <- log(x)
    lwr_t <- log(lwr)
    upr_t <- log(upr)
    xlim_t <- log(xlim)
  } else {
    x_t <- x
    lwr_t <- lwr
    upr_t <- upr
    xlim_t <- xlim
  }

  # Set up plot area
  gg_plt <- ggplot(data = tbl_df) +
    theme(
      panel.background = element_rect(fill = "transparent", color = NA_character_),
      plot.background = element_rect(fill = "transparent", color = NA_character_),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.line.x = element_line(),
      axis.text = element_text(size = font_size),
      legend.position = "none",
      plot.margin = margin(0, 0.1, 0.05, 0, "npc")
    ) +
    scale_x_continuous(
      transform = ifelse(logx, "log", "identity"),
      limits = xlim,
      breaks = x_at,
      labels = x_labels,
      expand = c(0.01, 0)
    ) +
    scale_y_continuous(
      limits = c(0, nrow(mat_strings) + 1),
      breaks = NULL,
      expand = c(0, 0)
    ) +
    coord_cartesian(clip = "off")

  if (is.null(ggtheme)) {
    gg_plt <- gg_plt + annotate(
      "rect",
      xmin = xlim[1],
      xmax = xlim[2],
      ymin = 0,
      ymax = nrows_body + 0.5,
      fill = "grey92"
    )
  }

  if (!is.null(vline)) {
    # Set default forest header
    if (is.null(forest_header)) {
      forest_header <- c(
        paste(if (length(arms) == 2) arms[1] else "Comparison", "Better", sep = "\n"),
        paste(if (length(arms) == 2) arms[2] else "Treatment", "Better", sep = "\n")
      )
    }

    # Add vline and forest header labels
    mid_pts <- if (logx) {
      c(exp(mean(log(c(xlim[1], vline)))), exp(mean(log(c(vline, xlim[2])))))
    } else {
      c(mean(c(xlim[1], vline)), mean(c(vline, xlim[2])))
    }
    gg_plt <- gg_plt +
      annotate(
        "segment",
        x = vline, xend = vline, y = 0, yend = nrows_body + 0.5
      ) +
      annotate(
        "text",
        x = mid_pts[1], y = nrows_body + 1.25,
        label = forest_header[1],
        size = font_size / .pt,
        lineheight = 0.9
      ) +
      annotate(
        "text",
        x = mid_pts[2], y = nrows_body + 1.25,
        label = forest_header[2],
        size = font_size / .pt,
        lineheight = 0.9
      )
  }

  # Add points to plot
  if (any(!is.na(x_t))) {
    x_t[x < xlim[1] | x > xlim[2]] <- NA
    gg_plt <- gg_plt + geom_point(
      x = x_t,
      y = row_num,
      color = col,
      aes(size = sym_size),
      na.rm = TRUE
    )
  }

  for (i in seq_len(nrow(tbl_df))) {
    # Determine which arrow(s) to add to CI lines
    which_arrow <- c(lwr_t[i] < xlim_t[1], upr_t[i] > xlim_t[2])
    which_arrow <- dplyr::case_when(
      all(which_arrow) ~ "both",
      which_arrow[1] ~ "first",
      which_arrow[2] ~ "last",
      TRUE ~ NA
    )

    # Add CI lines
    gg_plt <- gg_plt +
      if (!is.na(which_arrow)) {
        annotate(
          "segment",
          x = if (!which_arrow %in% c("first", "both")) lwr[i] else xlim[1],
          xend = if (!which_arrow %in% c("last", "both")) upr[i] else xlim[2],
          y = row_num[i], yend = row_num[i],
          color = if (length(col) == 1) col else col[i],
          arrow = arrow(length = unit(0.05, "npc"), ends = which_arrow),
          na.rm = TRUE
        )
      } else {
        annotate(
          "segment",
          x = lwr[i], xend = upr[i],
          y = row_num[i], yend = row_num[i],
          color = if (length(col) == 1) col else col[i],
          na.rm = TRUE
        )
      }
  }

  # Apply custom ggtheme to plot
  if (!is.null(ggtheme)) gg_plt <- gg_plt + ggtheme

  if (as_list) {
    list(
      table = gg_table,
      plot = gg_plt
    )
  } else {
    cowplot::plot_grid(
      gg_table,
      gg_plt,
      align = "h",
      axis = "tblr",
      rel_widths = c(1 - rel_width_forest, rel_width_forest)
    )
  }
}

#' Forest Plot Grob
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' @inheritParams g_forest
#' @param tbl ([rtables::rtable()])
#' @param x (`numeric`)\cr coordinate of point.
#' @param lower,upper (`numeric`)\cr lower/upper bound of the confidence interval.
#' @param symbol_size (`numeric`)\cr vector with relative size for plot symbol.
#' If `NULL`, the same symbol size is used.
#'
#' @details
#' The heights get automatically determined.
#'
#' @noRd
#'
#' @examples
#' tbl <- rtable(
#'   header = rheader(
#'     rrow("", "E", rcell("CI", colspan = 2), "N"),
#'     rrow("", "A", "B", "C", "D")
#'   ),
#'   rrow("row 1", 1, 0.8, 1.1, 16),
#'   rrow("row 2", 1.4, 0.8, 1.6, 25),
#'   rrow("row 3", 1.2, 0.8, 1.6, 36)
#' )
#'
#' x <- c(1, 1.4, 1.2)
#' lower <- c(0.8, 0.8, 0.8)
#' upper <- c(1.1, 1.6, 1.6)
#' # numeric vector with multiplication factor to scale each circle radius
#' # default radius is 1/3.5 lines
#' symbol_scale <- c(1, 1.25, 1.5)
#'
#' # Internal function - forest_grob
#' \donttest{
#' p <- forest_grob(tbl, x, lower, upper,
#'   vline = 1, forest_header = c("A", "B"),
#'   x_at = c(.1, 1, 10), xlim = c(0.1, 10), logx = TRUE, symbol_size = symbol_scale,
#'   vp = grid::plotViewport(margins = c(1, 1, 1, 1))
#' )
#'
#' draw_grob(p)
#' }
forest_grob <- function(tbl,
                        x,
                        lower,
                        upper,
                        vline,
                        forest_header,
                        xlim = NULL,
                        logx = FALSE,
                        x_at = NULL,
                        width_row_names = NULL,
                        width_columns = NULL,
                        width_forest = grid::unit(1, "null"),
                        symbol_size = NULL,
                        col = "blue",
                        name = NULL,
                        gp = NULL,
                        vp = NULL) {
  lifecycle::deprecate_warn(
    "0.9.4", "forest_grob()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  nr <- nrow(tbl)
  if (is.null(vline)) {
    checkmate::assert_true(is.null(forest_header))
  } else {
    checkmate::assert_number(vline)
    checkmate::assert_character(forest_header, len = 2, null.ok = TRUE)
  }

  checkmate::assert_numeric(x, len = nr)
  checkmate::assert_numeric(lower, len = nr)
  checkmate::assert_numeric(upper, len = nr)
  checkmate::assert_numeric(symbol_size, len = nr, null.ok = TRUE)
  checkmate::assert_character(col)

  if (is.null(symbol_size)) {
    symbol_size <- rep(1, nr)
  }

  if (is.null(xlim)) {
    r <- range(c(x, lower, upper), na.rm = TRUE)
    xlim <- r + c(-0.05, 0.05) * diff(r)
  }

  if (logx) {
    if (is.null(x_at)) {
      x_at <- pretty(log(stats::na.omit(c(x, lower, upper))))
      x_labels <- exp(x_at)
    } else {
      x_labels <- x_at
      x_at <- log(x_at)
    }
    xlim <- log(xlim)
    x <- log(x)
    lower <- log(lower)
    upper <- log(upper)
    if (!is.null(vline)) {
      vline <- log(vline)
    }
  } else {
    x_labels <- TRUE
  }

  data_forest_vp <- grid::dataViewport(xlim, c(0, 1))

  # Get table content as matrix form.
  mf <- matrix_form(tbl)

  # Use `rtables` indent_string eventually.
  mf$strings[, 1] <- paste0(
    strrep("    ", c(rep(0, attr(mf, "nrow_header")), mf$row_info$indent)),
    mf$strings[, 1]
  )

  n_header <- attr(mf, "nrow_header")

  if (any(mf$display[, 1] == FALSE)) stop("row names need to be always displayed")

  # Pre-process the data to be used in lapply and cell_in_rows.
  to_args_for_cell_in_rows_fun <- function(part = c("body", "header"),
                                           underline_colspan = FALSE) {
    part <- match.arg(part)
    if (part == "body") {
      mat_row_indices <- seq_len(nrow(tbl)) + n_header
      row_ind_offset <- -n_header
    } else {
      mat_row_indices <- seq_len(n_header)
      row_ind_offset <- 0
    }

    lapply(mat_row_indices, function(i) {
      disp <- mf$display[i, -1]
      list(
        row_name = mf$strings[i, 1],
        cells = mf$strings[i, -1][disp],
        cell_spans = mf$spans[i, -1][disp],
        row_index = i + row_ind_offset,
        underline_colspan = underline_colspan
      )
    })
  }

  args_header <- to_args_for_cell_in_rows_fun("header", underline_colspan = TRUE)
  args_body <- to_args_for_cell_in_rows_fun("body", underline_colspan = FALSE)

  grid::gTree(
    name = name,
    children = grid::gList(
      grid::gTree(
        children = do.call(grid::gList, lapply(args_header, do.call, what = cell_in_rows)),
        vp = grid::vpPath("vp_table_layout", "vp_header")
      ),
      grid::gTree(
        children = do.call(grid::gList, lapply(args_body, do.call, what = cell_in_rows)),
        vp = grid::vpPath("vp_table_layout", "vp_body")
      ),
      grid::linesGrob(
        grid::unit(c(0, 1), "npc"),
        y = grid::unit(c(.5, .5), "npc"),
        vp = grid::vpPath("vp_table_layout", "vp_spacer")
      ),
      # forest part
      if (is.null(vline)) {
        NULL
      } else {
        grid::gTree(
          children = grid::gList(
            grid::gTree(
              children = grid::gList(
                # this may overflow, to fix, look here
                # https://stackoverflow.com/questions/33623169/add-multi-line-footnote-to-tablegrob-while-using-gridextra-in-r # nolint
                grid::textGrob(
                  forest_header[1],
                  x = grid::unit(vline, "native") - grid::unit(1, "lines"),
                  just = c("right", "center")
                ),
                grid::textGrob(
                  forest_header[2],
                  x = grid::unit(vline, "native") + grid::unit(1, "lines"),
                  just = c("left", "center")
                )
              ),
              vp = grid::vpStack(grid::viewport(layout.pos.col = ncol(tbl) + 2), data_forest_vp)
            )
          ),
          vp = grid::vpPath("vp_table_layout", "vp_header")
        )
      },
      grid::gTree(
        children = grid::gList(
          grid::gTree(
            children = grid::gList(
              grid::rectGrob(gp = grid::gpar(col = "gray90", fill = "gray90")),
              if (is.null(vline)) {
                NULL
              } else {
                grid::linesGrob(
                  x = grid::unit(rep(vline, 2), "native"),
                  y = grid::unit(c(0, 1), "npc"),
                  gp = grid::gpar(lwd = 2),
                  vp = data_forest_vp
                )
              },
              grid::xaxisGrob(at = x_at, label = x_labels, vp = data_forest_vp)
            ),
            vp = grid::viewport(layout.pos.col = ncol(tbl) + 2)
          )
        ),
        vp = grid::vpPath("vp_table_layout", "vp_body")
      ),
      grid::gTree(
        children = do.call(
          grid::gList,
          Map(
            function(xi, li, ui, row_index, size_i, col) {
              forest_dot_line(
                xi,
                li,
                ui,
                row_index,
                xlim,
                symbol_size = size_i,
                col = col,
                datavp = data_forest_vp
              )
            },
            x,
            lower,
            upper,
            seq_along(x),
            symbol_size,
            col,
            USE.NAMES = FALSE
          )
        ),
        vp = grid::vpPath("vp_table_layout", "vp_body")
      )
    ),
    childrenvp = forest_viewport(tbl, width_row_names, width_columns, width_forest),
    vp = vp,
    gp = gp
  )
}

cell_in_rows <- function(row_name,
                         cells,
                         cell_spans,
                         row_index,
                         underline_colspan = FALSE) {
  checkmate::assert_string(row_name)
  checkmate::assert_character(cells, min.len = 1, any.missing = FALSE)
  checkmate::assert_numeric(cell_spans, len = length(cells), any.missing = FALSE)
  checkmate::assert_number(row_index)
  checkmate::assert_flag(underline_colspan)

  vp_name_rn <- paste0("rowname-", row_index)
  g_rowname <- if (!is.null(row_name) && row_name != "") {
    grid::textGrob(
      name = vp_name_rn,
      label = row_name,
      x = grid::unit(0, "npc"),
      just = c("left", "center"),
      vp = grid::vpPath(paste0("rowname-", row_index))
    )
  } else {
    NULL
  }

  gl_cols <- if (!(length(cells) > 0)) {
    list(NULL)
  } else {
    j <- 1 # column index of cell

    lapply(seq_along(cells), function(k) {
      cell_ascii <- cells[[k]]
      cs <- cell_spans[[k]]

      if (is.na(cell_ascii) || is.null(cell_ascii)) {
        cell_ascii <- "NA"
      }

      cell_name <- paste0("g-cell-", row_index, "-", j)

      cell_grobs <- if (identical(cell_ascii, "")) {
        NULL
      } else {
        if (cs == 1) {
          grid::textGrob(
            label = cell_ascii,
            name = cell_name,
            vp = grid::vpPath(paste0("cell-", row_index, "-", j))
          )
        } else {
          # +1 because of rowname
          vp_joined_cols <- grid::viewport(layout.pos.row = row_index, layout.pos.col = seq(j + 1, j + cs))

          lab <- grid::textGrob(
            label = cell_ascii,
            name = cell_name,
            vp = vp_joined_cols
          )

          if (!underline_colspan || grepl("^[[:space:]]*$", cell_ascii)) {
            lab
          } else {
            grid::gList(
              lab,
              grid::linesGrob(
                x = grid::unit.c(grid::unit(.2, "lines"), grid::unit(1, "npc") - grid::unit(.2, "lines")),
                y = grid::unit(c(0, 0), "npc"),
                vp = vp_joined_cols
              )
            )
          }
        }
      }
      j <<- j + cs

      cell_grobs
    })
  }

  grid::gList(
    g_rowname,
    do.call(grid::gList, gl_cols)
  )
}

#' Graphic Object: Forest Dot Line
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Calculate the `grob` corresponding to the dot line within the forest plot.
#'
#' @noRd
forest_dot_line <- function(x,
                            lower,
                            upper,
                            row_index,
                            xlim,
                            symbol_size = 1,
                            col = "blue",
                            datavp) {
  lifecycle::deprecate_warn(
    "0.9.4", "forest_dot_line()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  ci <- c(lower, upper)
  if (any(!is.na(c(x, ci)))) {
    # line
    y <- grid::unit(c(0.5, 0.5), "npc")

    g_line <- if (all(!is.na(ci)) && ci[2] > xlim[1] && ci[1] < xlim[2]) {
      # -
      if (ci[1] >= xlim[1] && ci[2] <= xlim[2]) {
        grid::linesGrob(x = grid::unit(c(ci[1], ci[2]), "native"), y = y)
      } else if (ci[1] < xlim[1] && ci[2] > xlim[2]) {
        # <->
        grid::linesGrob(
          x = grid::unit(xlim, "native"),
          y = y,
          arrow = grid::arrow(angle = 30, length = grid::unit(0.5, "lines"), ends = "both")
        )
      } else if (ci[1] < xlim[1] && ci[2] <= xlim[2]) {
        # <-
        grid::linesGrob(
          x = grid::unit(c(xlim[1], ci[2]), "native"),
          y = y,
          arrow = grid::arrow(angle = 30, length = grid::unit(0.5, "lines"), ends = "first")
        )
      } else if (ci[1] >= xlim[1] && ci[2] > xlim[2]) {
        # ->
        grid::linesGrob(
          x = grid::unit(c(ci[1], xlim[2]), "native"),
          y = y,
          arrow = grid::arrow(angle = 30, length = grid::unit(0.5, "lines"), ends = "last")
        )
      }
    } else {
      NULL
    }

    g_circle <- if (!is.na(x) && x >= xlim[1] && x <= xlim[2]) {
      grid::circleGrob(
        x = grid::unit(x, "native"),
        y = y,
        r = grid::unit(1 / 3.5 * symbol_size, "lines"),
        name = "point"
      )
    } else {
      NULL
    }

    grid::gTree(
      children = grid::gList(
        grid::gTree(
          children = grid::gList(
            grid::gList(
              g_line,
              g_circle
            )
          ),
          vp = datavp,
          gp = grid::gpar(col = col, fill = col)
        )
      ),
      vp = grid::vpPath(paste0("forest-", row_index))
    )
  } else {
    NULL
  }
}

#' Create a Viewport Tree for the Forest Plot
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' @param tbl (`rtable`)
#' @param width_row_names (`grid::unit`)\cr Width of row names
#' @param width_columns (`grid::unit`)\cr Width of column spans
#' @param width_forest (`grid::unit`)\cr Width of the forest plot
#' @param gap_column (`grid::unit`)\cr Gap width between the columns
#' @param gap_header (`grid::unit`)\cr Gap width between the header
#' @param mat_form matrix print form of the table
#' @return A viewport tree.
#'
#' @examples
#' library(grid)
#'
#' tbl <- rtable(
#'   header = rheader(
#'     rrow("", "E", rcell("CI", colspan = 2)),
#'     rrow("", "A", "B", "C")
#'   ),
#'   rrow("row 1", 1, 0.8, 1.1),
#'   rrow("row 2", 1.4, 0.8, 1.6),
#'   rrow("row 3", 1.2, 0.8, 1.2)
#' )
#'
#' \donttest{
#' v <- forest_viewport(tbl)
#'
#' grid::grid.newpage()
#' showViewport(v)
#' }
#'
#' @export
forest_viewport <- function(tbl,
                            width_row_names = NULL,
                            width_columns = NULL,
                            width_forest = grid::unit(1, "null"),
                            gap_column = grid::unit(1, "lines"),
                            gap_header = grid::unit(1, "lines"),
                            mat_form = NULL) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "forest_viewport()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  checkmate::assert_class(tbl, "VTableTree")
  checkmate::assert_true(grid::is.unit(width_forest))
  if (!is.null(width_row_names)) {
    checkmate::assert_true(grid::is.unit(width_row_names))
  }
  if (!is.null(width_columns)) {
    checkmate::assert_true(grid::is.unit(width_columns))
  }

  if (is.null(mat_form)) mat_form <- matrix_form(tbl)

  mat_form$strings[!mat_form$display] <- ""

  nr <- nrow(tbl)
  nc <- ncol(tbl)
  nr_h <- attr(mat_form, "nrow_header")

  if (is.null(width_row_names) || is.null(width_columns)) {
    tbl_widths <- formatters::propose_column_widths(mat_form)
    strs_with_width <- strrep("x", tbl_widths) # that works for mono spaced fonts
    if (is.null(width_row_names)) width_row_names <- grid::stringWidth(strs_with_width[1])
    if (is.null(width_columns)) width_columns <- grid::stringWidth(strs_with_width[-1])
  }

  # Widths for row name, cols, forest.
  widths <- grid::unit.c(
    width_row_names + gap_column,
    width_columns + gap_column,
    width_forest
  )

  n_lines_per_row <- apply(
    X = mat_form$strings,
    MARGIN = 1,
    FUN = function(row) {
      tmp <- vapply(
        gregexpr("\n", row, fixed = TRUE),
        attr, numeric(1),
        "match.length"
      ) + 1
      max(c(tmp, 1))
    }
  )

  i_header <- seq_len(nr_h)

  height_body_rows <- grid::unit(n_lines_per_row[-i_header] * 1.2, "lines")
  height_header_rows <- grid::unit(n_lines_per_row[i_header] * 1.2, "lines")

  height_body <- grid::unit(sum(n_lines_per_row[-i_header]) * 1.2, "lines")
  height_header <- grid::unit(sum(n_lines_per_row[i_header]) * 1.2, "lines")

  nc_g <- nc + 2 # number of columns incl. row names and forest

  vp_tbl <- grid::vpTree(
    parent = grid::viewport(
      name = "vp_table_layout",
      layout = grid::grid.layout(
        nrow = 3, ncol = 1,
        heights = grid::unit.c(height_header, gap_header, height_body)
      )
    ),
    children = grid::vpList(
      vp_forest_table_part(nr_h, nc_g, 1, 1, widths, height_header_rows, "vp_header"),
      vp_forest_table_part(nr, nc_g, 3, 1, widths, height_body_rows, "vp_body"),
      grid::viewport(name = "vp_spacer", layout.pos.row = 2, layout.pos.col = 1)
    )
  )
  vp_tbl
}

#' Viewport Forest Plot: Table Part
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Prepares a viewport for the table included in the forest plot.
#'
#' @noRd
vp_forest_table_part <- function(nrow,
                                 ncol,
                                 l_row,
                                 l_col,
                                 widths,
                                 heights,
                                 name) {
  lifecycle::deprecate_warn(
    "0.9.4", "vp_forest_table_part()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  grid::vpTree(
    grid::viewport(
      name = name,
      layout.pos.row = l_row,
      layout.pos.col = l_col,
      layout = grid::grid.layout(nrow = nrow, ncol = ncol, widths = widths, heights = heights)
    ),
    children = grid::vpList(
      do.call(
        grid::vpList,
        lapply(
          seq_len(nrow), function(i) {
            grid::viewport(layout.pos.row = i, layout.pos.col = 1, name = paste0("rowname-", i))
          }
        )
      ),
      do.call(
        grid::vpList,
        apply(
          expand.grid(seq_len(nrow), seq_len(ncol - 2)),
          1,
          function(x) {
            i <- x[1]
            j <- x[2]
            grid::viewport(layout.pos.row = i, layout.pos.col = j + 1, name = paste0("cell-", i, "-", j))
          }
        )
      ),
      do.call(
        grid::vpList,
        lapply(
          seq_len(nrow),
          function(i) {
            grid::viewport(layout.pos.row = i, layout.pos.col = ncol, name = paste0("forest-", i))
          }
        )
      )
    )
  )
}

#' Forest Rendering
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Renders the forest grob.
#'
#' @noRd
grid.forest <- function(...) { # nolint
  lifecycle::deprecate_warn(
    "0.9.4", "grid.forest()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  grid::grid.draw(forest_grob(...))
}

#' Multivariate Logistic Regression Table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Layout-creating function which summarizes a logistic variable regression for binary outcome with
#' categorical/continuous covariates in model statement. For each covariate category (if categorical)
#' or specified values (if continuous), present degrees of freedom, regression parameter estimate and
#' standard error (SE) relative to reference group or category. Report odds ratios for each covariate
#' category or specified values and corresponding Wald confidence intervals as default but allow user
#' to specify other confidence levels. Report p-value for Wald chi-square test of the null hypothesis
#' that covariate has no effect on response in model containing all specified covariates.
#' Allow option to include one two-way interaction and present similar output for
#' each interaction degree of freedom.
#'
#' @inheritParams argument_convention
#' @param drop_and_remove_str (`character`)\cr string to be dropped and removed.
#'
#' @return A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
#'   Adding this function to an `rtable` layout will add a logistic regression variable summary to the table layout.
#'
#' @note For the formula, the variable names need to be standard `data.frame` column names without
#'   special characters.
#'
#' @examples
#' library(dplyr)
#' library(broom)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' df <- tidy(mod1, conf_level = 0.99)
#' df2 <- tidy(mod2, conf_level = 0.99)
#'
#' # flagging empty strings with "_"
#' df <- df_explicit_na(df, na_level = "_")
#' df2 <- df_explicit_na(df2, na_level = "_")
#'
#' result1 <- basic_table() %>%
#'   summarize_logistic(
#'     conf_level = 0.95,
#'     drop_and_remove_str = "_"
#'   ) %>%
#'   build_table(df = df)
#' result1
#'
#' result2 <- basic_table() %>%
#'   summarize_logistic(
#'     conf_level = 0.95,
#'     drop_and_remove_str = "_"
#'   ) %>%
#'   build_table(df = df2)
#' result2
#'
#' @export
#' @order 1
summarize_logistic <- function(lyt,
                               conf_level,
                               drop_and_remove_str = "",
                               .indent_mods = NULL) {
  # checks
  checkmate::assert_string(drop_and_remove_str)

  sum_logistic_variable_test <- logistic_summary_by_flag("is_variable_summary")
  sum_logistic_term_estimates <- logistic_summary_by_flag("is_term_summary", .indent_mods = .indent_mods)
  sum_logistic_odds_ratios <- logistic_summary_by_flag("is_reference_summary", .indent_mods = .indent_mods)
  split_fun <- drop_and_remove_levels(drop_and_remove_str)

  lyt <- logistic_regression_cols(lyt, conf_level = conf_level)
  lyt <- split_rows_by(lyt, var = "variable", labels_var = "variable_label", split_fun = split_fun)
  lyt <- sum_logistic_variable_test(lyt)
  lyt <- split_rows_by(lyt, var = "term", labels_var = "term_label", split_fun = split_fun)
  lyt <- sum_logistic_term_estimates(lyt)
  lyt <- split_rows_by(lyt, var = "interaction", labels_var = "interaction_label", split_fun = split_fun)
  lyt <- split_rows_by(lyt, var = "reference", labels_var = "reference_label", split_fun = split_fun)
  lyt <- sum_logistic_odds_ratios(lyt)
  lyt
}

#' Fit for Logistic Regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Fit a (conditional) logistic regression model.
#'
#' @inheritParams argument_convention
#' @param data (`data.frame`)\cr the data frame on which the model was fit.
#' @param response_definition (`string`)\cr the definition of what an event is in terms of `response`.
#'   This will be used when fitting the (conditional) logistic regression model on the left hand
#'   side of the formula.
#'
#' @return A fitted logistic regression model.
#'
#' @section Model Specification:
#'
#' The `variables` list needs to include the following elements:
#'   * `arm`: Treatment arm variable name.
#'   * `response`: The response arm variable name. Usually this is a 0/1 variable.
#'   * `covariates`: This is either `NULL` (no covariates) or a character vector of covariate variable names.
#'   * `interaction`: This is either `NULL` (no interaction) or a string of a single covariate variable name already
#'     included in `covariates`. Then the interaction with the treatment arm is included in the model.
#'
#' @examples
#' library(dplyr)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' @export
fit_logistic <- function(data,
                         variables = list(
                           response = "Response",
                           arm = "ARMCD",
                           covariates = NULL,
                           interaction = NULL,
                           strata = NULL
                         ),
                         response_definition = "response") {
  assert_df_with_variables(data, variables)
  checkmate::assert_subset(names(variables), c("response", "arm", "covariates", "interaction", "strata"))
  checkmate::assert_string(response_definition)
  checkmate::assert_true(grepl("response", response_definition))

  response_definition <- sub(
    pattern = "response",
    replacement = variables$response,
    x = response_definition,
    fixed = TRUE
  )
  form <- paste0(response_definition, " ~ ", variables$arm)
  if (!is.null(variables$covariates)) {
    form <- paste0(form, " + ", paste(variables$covariates, collapse = " + "))
  }
  if (!is.null(variables$interaction)) {
    checkmate::assert_string(variables$interaction)
    checkmate::assert_subset(variables$interaction, variables$covariates)
    form <- paste0(form, " + ", variables$arm, ":", variables$interaction)
  }
  if (!is.null(variables$strata)) {
    strata_arg <- if (length(variables$strata) > 1) {
      paste0("I(interaction(", paste0(variables$strata, collapse = ", "), "))")
    } else {
      variables$strata
    }
    form <- paste0(form, "+ strata(", strata_arg, ")")
  }
  formula <- stats::as.formula(form)
  if (is.null(variables$strata)) {
    stats::glm(
      formula = formula,
      data = data,
      family = stats::binomial("logit")
    )
  } else {
    clogit_with_tryCatch(
      formula = formula,
      data = data,
      x = TRUE
    )
  }
}

#' Custom Tidy Method for Binomial GLM Results
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper method (for [broom::tidy()]) to prepare a data frame from a `glm` object
#' with `binomial` family.
#'
#' @inheritParams argument_convention
#' @param at (`NULL` or `numeric`)\cr optional values for the interaction variable. Otherwise the median is used.
#' @param x logistic regression model fitted by [stats::glm()] with "binomial" family.
#'
#' @return A `data.frame` containing the tidied model.
#'
#' @method tidy glm
#'
#' @seealso [h_logistic_regression] for relevant helper functions.
#'
#' @examples
#' library(dplyr)
#' library(broom)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' df <- tidy(mod1, conf_level = 0.99)
#' df2 <- tidy(mod2, conf_level = 0.99)
#'
#' @export
tidy.glm <- function(x, # nolint
                     conf_level = 0.95,
                     at = NULL,
                     ...) {
  checkmate::assert_class(x, "glm")
  checkmate::assert_set_equal(x$family$family, "binomial")

  terms_name <- attr(stats::terms(x), "term.labels")
  xs_class <- attr(x$terms, "dataClasses")
  interaction <- terms_name[which(!terms_name %in% names(xs_class))]
  df <- if (length(interaction) == 0) {
    h_logistic_simple_terms(
      x = terms_name,
      fit_glm = x,
      conf_level = conf_level
    )
  } else {
    h_logistic_inter_terms(
      x = terms_name,
      fit_glm = x,
      conf_level = conf_level,
      at = at
    )
  }
  for (var in c("variable", "term", "interaction", "reference")) {
    df[[var]] <- factor(df[[var]], levels = unique(df[[var]]))
  }
  df
}

#' Logistic Regression Multivariate Column Layout Function
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Layout-creating function which creates a multivariate column layout summarizing logistic
#' regression results. This function is a wrapper for [rtables::split_cols_by_multivar()].
#'
#' @inheritParams argument_convention
#'
#' @return A layout object suitable for passing to further layouting functions. Adding this
#'   function to an `rtable` layout will split the table into columns corresponding to
#'   statistics `df`, `estimate`, `std_error`, `odds_ratio`, `ci`, and `pvalue`.
#'
#' @export
logistic_regression_cols <- function(lyt,
                                     conf_level = 0.95) {
  vars <- c("df", "estimate", "std_error", "odds_ratio", "ci", "pvalue")
  var_labels <- c(
    df = "Degrees of Freedom",
    estimate = "Parameter Estimate",
    std_error = "Standard Error",
    odds_ratio = "Odds Ratio",
    ci = paste("Wald", f_conf_level(conf_level)),
    pvalue = "p-value"
  )
  split_cols_by_multivar(
    lyt = lyt,
    vars = vars,
    varlabels = var_labels
  )
}

#' Logistic Regression Summary Table Constructor Function
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Constructor for content functions to be used in [`summarize_logistic()`] to summarize
#' logistic regression results. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @inheritParams argument_convention
#' @param flag_var (`string`)\cr variable name identifying which row should be used in this
#'   content function.
#'
#' @return A content function.
#'
#' @export
logistic_summary_by_flag <- function(flag_var, na_str = default_na_str(), .indent_mods = NULL) {
  checkmate::assert_string(flag_var)
  function(lyt) {
    cfun_list <- list(
      df = cfun_by_flag("df", flag_var, format = "xx.", .indent_mods = .indent_mods),
      estimate = cfun_by_flag("estimate", flag_var, format = "xx.xxx", .indent_mods = .indent_mods),
      std_error = cfun_by_flag("std_error", flag_var, format = "xx.xxx", .indent_mods = .indent_mods),
      odds_ratio = cfun_by_flag("odds_ratio", flag_var, format = ">999.99", .indent_mods = .indent_mods),
      ci = cfun_by_flag("ci", flag_var, format = format_extreme_values_ci(2L), .indent_mods = .indent_mods),
      pvalue = cfun_by_flag("pvalue", flag_var, format = "x.xxxx | (<0.0001)", .indent_mods = .indent_mods)
    )
    summarize_row_groups(
      lyt = lyt,
      cfun = cfun_list,
      na_str = na_str
    )
  }
}

#' Helper Functions for Multivariate Logistic Regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions used in calculations for logistic regression.
#'
#' @inheritParams argument_convention
#' @param fit_glm (`glm`)\cr logistic regression model fitted by [stats::glm()] with "binomial" family.
#'   Limited functionality is also available for conditional logistic regression models fitted by
#'   [survival::clogit()], currently this is used only by [extract_rsp_biomarkers()].
#' @param x (`string` or `character`)\cr a variable or interaction term in `fit_glm` (depending on the
#'   helper function).
#'
#' @examples
#' library(dplyr)
#' library(broom)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' @name h_logistic_regression
NULL

#' @describeIn h_logistic_regression Helper function to extract interaction variable names from a fitted
#'   model assuming only one interaction term.
#'
#' @return Vector of names of interaction variables.
#'
#' @export
h_get_interaction_vars <- function(fit_glm) {
  checkmate::assert_class(fit_glm, "glm")
  terms_name <- attr(stats::terms(fit_glm), "term.labels")
  terms_order <- attr(stats::terms(fit_glm), "order")
  interaction_term <- terms_name[terms_order == 2]
  checkmate::assert_string(interaction_term)
  strsplit(interaction_term, split = ":")[[1]]
}

#' @describeIn h_logistic_regression Helper function to get the right coefficient name from the
#'   interaction variable names and the given levels. The main value here is that the order
#'   of first and second variable is checked in the `interaction_vars` input.
#'
#' @param interaction_vars (`character` of length 2)\cr interaction variable names.
#' @param first_var_with_level (`character` of length 2)\cr the first variable name with
#'   the interaction level.
#' @param second_var_with_level (`character` of length 2)\cr the second variable name with
#'   the interaction level.
#'
#' @return Name of coefficient.
#'
#' @export
h_interaction_coef_name <- function(interaction_vars,
                                    first_var_with_level,
                                    second_var_with_level) {
  checkmate::assert_character(interaction_vars, len = 2, any.missing = FALSE)
  checkmate::assert_character(first_var_with_level, len = 2, any.missing = FALSE)
  checkmate::assert_character(second_var_with_level, len = 2, any.missing = FALSE)
  checkmate::assert_subset(c(first_var_with_level[1], second_var_with_level[1]), interaction_vars)

  first_name <- paste(first_var_with_level, collapse = "")
  second_name <- paste(second_var_with_level, collapse = "")
  if (first_var_with_level[1] == interaction_vars[1]) {
    paste(first_name, second_name, sep = ":")
  } else if (second_var_with_level[1] == interaction_vars[1]) {
    paste(second_name, first_name, sep = ":")
  }
}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates
#'   for the case when both the odds ratio and the interaction variable are categorical.
#'
#' @param odds_ratio_var (`string`)\cr the odds ratio variable.
#' @param interaction_var (`string`)\cr the interaction variable.
#'
#' @return Odds ratio.
#'
#' @export
h_or_cat_interaction <- function(odds_ratio_var,
                                 interaction_var,
                                 fit_glm,
                                 conf_level = 0.95) {
  interaction_vars <- h_get_interaction_vars(fit_glm)
  checkmate::assert_string(odds_ratio_var)
  checkmate::assert_string(interaction_var)
  checkmate::assert_subset(c(odds_ratio_var, interaction_var), interaction_vars)
  checkmate::assert_vector(interaction_vars, len = 2)

  xs_level <- fit_glm$xlevels
  xs_coef <- stats::coef(fit_glm)
  xs_vcov <- stats::vcov(fit_glm)
  y <- list()
  for (var_level in xs_level[[odds_ratio_var]][-1]) {
    x <- list()
    for (ref_level in xs_level[[interaction_var]]) {
      coef_names <- paste0(odds_ratio_var, var_level)
      if (ref_level != xs_level[[interaction_var]][1]) {
        interaction_coef_name <- h_interaction_coef_name(
          interaction_vars,
          c(odds_ratio_var, var_level),
          c(interaction_var, ref_level)
        )
        coef_names <- c(
          coef_names,
          interaction_coef_name
        )
      }
      if (length(coef_names) > 1) {
        ones <- t(c(1, 1))
        est <- as.numeric(ones %*% xs_coef[coef_names])
        se <- sqrt(as.numeric(ones %*% xs_vcov[coef_names, coef_names] %*% t(ones)))
      } else {
        est <- xs_coef[coef_names]
        se <- sqrt(as.numeric(xs_vcov[coef_names, coef_names]))
      }
      or <- exp(est)
      ci <- exp(est + c(lcl = -1, ucl = 1) * stats::qnorm((1 + conf_level) / 2) * se)
      x[[ref_level]] <- list(or = or, ci = ci)
    }
    y[[var_level]] <- x
  }
  y
}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates
#'   for the case when either the odds ratio or the interaction variable is continuous.
#'
#' @param at (`NULL` or `numeric`)\cr optional values for the interaction variable. Otherwise
#'   the median is used.
#'
#' @return Odds ratio.
#'
#' @note We don't provide a function for the case when both variables are continuous because
#'   this does not arise in this table, as the treatment arm variable will always be involved
#'   and categorical.
#'
#' @export
h_or_cont_interaction <- function(odds_ratio_var,
                                  interaction_var,
                                  fit_glm,
                                  at = NULL,
                                  conf_level = 0.95) {
  interaction_vars <- h_get_interaction_vars(fit_glm)
  checkmate::assert_string(odds_ratio_var)
  checkmate::assert_string(interaction_var)
  checkmate::assert_subset(c(odds_ratio_var, interaction_var), interaction_vars)
  checkmate::assert_vector(interaction_vars, len = 2)
  checkmate::assert_numeric(at, min.len = 1, null.ok = TRUE, any.missing = FALSE)
  xs_level <- fit_glm$xlevels
  xs_coef <- stats::coef(fit_glm)
  xs_vcov <- stats::vcov(fit_glm)
  xs_class <- attr(fit_glm$terms, "dataClasses")
  model_data <- fit_glm$model
  if (!is.null(at)) {
    checkmate::assert_set_equal(xs_class[interaction_var], "numeric")
  }
  y <- list()
  if (xs_class[interaction_var] == "numeric") {
    if (is.null(at)) {
      at <- ceiling(stats::median(model_data[[interaction_var]]))
    }

    for (var_level in xs_level[[odds_ratio_var]][-1]) {
      x <- list()
      for (increment in at) {
        coef_names <- paste0(odds_ratio_var, var_level)
        if (increment != 0) {
          interaction_coef_name <- h_interaction_coef_name(
            interaction_vars,
            c(odds_ratio_var, var_level),
            c(interaction_var, "")
          )
          coef_names <- c(
            coef_names,
            interaction_coef_name
          )
        }
        if (length(coef_names) > 1) {
          xvec <- t(c(1, increment))
          est <- as.numeric(xvec %*% xs_coef[coef_names])
          se <- sqrt(as.numeric(xvec %*% xs_vcov[coef_names, coef_names] %*% t(xvec)))
        } else {
          est <- xs_coef[coef_names]
          se <- sqrt(as.numeric(xs_vcov[coef_names, coef_names]))
        }
        or <- exp(est)
        ci <- exp(est + c(lcl = -1, ucl = 1) * stats::qnorm((1 + conf_level) / 2) * se)
        x[[as.character(increment)]] <- list(or = or, ci = ci)
      }
      y[[var_level]] <- x
    }
  } else {
    checkmate::assert_set_equal(xs_class[odds_ratio_var], "numeric")
    checkmate::assert_set_equal(xs_class[interaction_var], "factor")
    for (var_level in xs_level[[interaction_var]]) {
      coef_names <- odds_ratio_var
      if (var_level != xs_level[[interaction_var]][1]) {
        interaction_coef_name <- h_interaction_coef_name(
          interaction_vars,
          c(odds_ratio_var, ""),
          c(interaction_var, var_level)
        )
        coef_names <- c(
          coef_names,
          interaction_coef_name
        )
      }
      if (length(coef_names) > 1) {
        xvec <- t(c(1, 1))
        est <- as.numeric(xvec %*% xs_coef[coef_names])
        se <- sqrt(as.numeric(xvec %*% xs_vcov[coef_names, coef_names] %*% t(xvec)))
      } else {
        est <- xs_coef[coef_names]
        se <- sqrt(as.numeric(xs_vcov[coef_names, coef_names]))
      }
      or <- exp(est)
      ci <- exp(est + c(lcl = -1, ucl = 1) * stats::qnorm((1 + conf_level) / 2) * se)
      y[[var_level]] <- list(or = or, ci = ci)
    }
  }
  y
}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates
#'   in case of an interaction. This is a wrapper for [h_or_cont_interaction()] and
#'   [h_or_cat_interaction()].
#'
#' @return Odds ratio.
#'
#' @export
h_or_interaction <- function(odds_ratio_var,
                             interaction_var,
                             fit_glm,
                             at = NULL,
                             conf_level = 0.95) {
  xs_class <- attr(fit_glm$terms, "dataClasses")
  if (any(xs_class[c(odds_ratio_var, interaction_var)] == "numeric")) {
    h_or_cont_interaction(
      odds_ratio_var,
      interaction_var,
      fit_glm,
      at = at,
      conf_level = conf_level
    )
  } else if (all(xs_class[c(odds_ratio_var, interaction_var)] == "factor")) {
    h_or_cat_interaction(
      odds_ratio_var,
      interaction_var,
      fit_glm,
      conf_level = conf_level
    )
  } else {
    stop("wrong interaction variable class, the interaction variable is not a numeric nor a factor")
  }
}

#' @describeIn h_logistic_regression Helper function to construct term labels from simple terms and the table
#'   of numbers of patients.
#'
#' @param terms (`character`)\cr simple terms.
#' @param table (`table`)\cr table containing numbers for terms.
#'
#' @return Term labels containing numbers of patients.
#'
#' @export
h_simple_term_labels <- function(terms,
                                 table) {
  checkmate::assert_true(is.table(table))
  checkmate::assert_multi_class(terms, classes = c("factor", "character"))
  terms <- as.character(terms)
  term_n <- table[terms]
  paste0(terms, ", n = ", term_n)
}

#' @describeIn h_logistic_regression Helper function to construct term labels from interaction terms and the table
#'   of numbers of patients.
#'
#' @param terms1 (`character`)\cr terms for first dimension (rows).
#' @param terms2 (`character`)\cr terms for second dimension (rows).
#' @param any (`flag`)\cr whether any of `term1` and `term2` can be fulfilled to count the
#'   number of patients. In that case they can only be scalar (strings).
#'
#' @return Term labels containing numbers of patients.
#'
#' @export
h_interaction_term_labels <- function(terms1,
                                      terms2,
                                      table,
                                      any = FALSE) {
  checkmate::assert_true(is.table(table))
  checkmate::assert_flag(any)
  checkmate::assert_multi_class(terms1, classes = c("factor", "character"))
  checkmate::assert_multi_class(terms2, classes = c("factor", "character"))
  terms1 <- as.character(terms1)
  terms2 <- as.character(terms2)
  if (any) {
    checkmate::assert_scalar(terms1)
    checkmate::assert_scalar(terms2)
    paste0(
      terms1, " or ", terms2, ", n = ",
      # Note that we double count in the initial sum the cell [terms1, terms2], therefore subtract.
      sum(c(table[terms1, ], table[, terms2])) - table[terms1, terms2]
    )
  } else {
    term_n <- table[cbind(terms1, terms2)]
    paste0(terms1, " * ", terms2, ", n = ", term_n)
  }
}

#' @describeIn h_logistic_regression Helper function to tabulate the main effect
#'   results of a (conditional) logistic regression model.
#'
#' @return Tabulated main effect results from a logistic regression model.
#'
#' @examples
#' h_glm_simple_term_extract("AGE", mod1)
#' h_glm_simple_term_extract("ARMCD", mod1)
#'
#' @export
h_glm_simple_term_extract <- function(x, fit_glm) {
  checkmate::assert_multi_class(fit_glm, c("glm", "clogit"))
  checkmate::assert_string(x)

  xs_class <- attr(fit_glm$terms, "dataClasses")
  xs_level <- fit_glm$xlevels
  xs_coef <- summary(fit_glm)$coefficients
  stats <- if (inherits(fit_glm, "glm")) {
    c("estimate" = "Estimate", "std_error" = "Std. Error", "pvalue" = "Pr(>|z|)")
  } else {
    c("estimate" = "coef", "std_error" = "se(coef)", "pvalue" = "Pr(>|z|)")
  }
  # Make sure x is not an interaction term.
  checkmate::assert_subset(x, names(xs_class))
  x_sel <- if (xs_class[x] == "numeric") x else paste0(x, xs_level[[x]][-1])
  x_stats <- as.data.frame(xs_coef[x_sel, stats, drop = FALSE], stringsAsFactors = FALSE)
  colnames(x_stats) <- names(stats)
  x_stats$estimate <- as.list(x_stats$estimate)
  x_stats$std_error <- as.list(x_stats$std_error)
  x_stats$pvalue <- as.list(x_stats$pvalue)
  x_stats$df <- as.list(1)
  if (xs_class[x] == "numeric") {
    x_stats$term <- x
    x_stats$term_label <- if (inherits(fit_glm, "glm")) {
      formatters::var_labels(fit_glm$data[x], fill = TRUE)
    } else {
      # We just fill in here with the `term` itself as we don't have the data available.
      x
    }
    x_stats$is_variable_summary <- FALSE
    x_stats$is_term_summary <- TRUE
  } else {
    checkmate::assert_class(fit_glm, "glm")
    # The reason is that we don't have the original data set in the `clogit` object
    # and therefore cannot determine the `x_numbers` here.
    x_numbers <- table(fit_glm$data[[x]])
    x_stats$term <- xs_level[[x]][-1]
    x_stats$term_label <- h_simple_term_labels(x_stats$term, x_numbers)
    x_stats$is_variable_summary <- FALSE
    x_stats$is_term_summary <- TRUE
    main_effects <- car::Anova(fit_glm, type = 3, test.statistic = "Wald")
    x_main <- data.frame(
      pvalue = main_effects[x, "Pr(>Chisq)", drop = TRUE],
      term = xs_level[[x]][1],
      term_label = paste("Reference", h_simple_term_labels(xs_level[[x]][1], x_numbers)),
      df = main_effects[x, "Df", drop = TRUE],
      stringsAsFactors = FALSE
    )
    x_main$pvalue <- as.list(x_main$pvalue)
    x_main$df <- as.list(x_main$df)
    x_main$estimate <- list(numeric(0))
    x_main$std_error <- list(numeric(0))
    if (length(xs_level[[x]][-1]) == 1) {
      x_main$pvalue <- list(numeric(0))
      x_main$df <- list(numeric(0))
    }
    x_main$is_variable_summary <- TRUE
    x_main$is_term_summary <- FALSE
    x_stats <- rbind(x_main, x_stats)
  }
  x_stats$variable <- x
  x_stats$variable_label <- if (inherits(fit_glm, "glm")) {
    formatters::var_labels(fit_glm$data[x], fill = TRUE)
  } else {
    x
  }
  x_stats$interaction <- ""
  x_stats$interaction_label <- ""
  x_stats$reference <- ""
  x_stats$reference_label <- ""
  rownames(x_stats) <- NULL
  x_stats[c(
    "variable",
    "variable_label",
    "term",
    "term_label",
    "interaction",
    "interaction_label",
    "reference",
    "reference_label",
    "estimate",
    "std_error",
    "df",
    "pvalue",
    "is_variable_summary",
    "is_term_summary"
  )]
}

#' @describeIn h_logistic_regression Helper function to tabulate the interaction term
#'   results of a logistic regression model.
#'
#' @return Tabulated interaction term results from a logistic regression model.
#'
#' @examples
#' h_glm_interaction_extract("ARMCD:AGE", mod2)
#'
#' @export
h_glm_interaction_extract <- function(x, fit_glm) {
  vars <- h_get_interaction_vars(fit_glm)
  xs_class <- attr(fit_glm$terms, "dataClasses")

  checkmate::assert_string(x)

  # Only take two-way interaction
  checkmate::assert_vector(vars, len = 2)

  # Only consider simple case: first variable in interaction is arm, a categorical variable
  checkmate::assert_disjunct(xs_class[vars[1]], "numeric")

  xs_level <- fit_glm$xlevels
  xs_coef <- summary(fit_glm)$coefficients
  main_effects <- car::Anova(fit_glm, type = 3, test.statistic = "Wald")
  stats <- c("estimate" = "Estimate", "std_error" = "Std. Error", "pvalue" = "Pr(>|z|)")
  v1_comp <- xs_level[[vars[1]]][-1]
  if (xs_class[vars[2]] == "numeric") {
    x_stats <- as.data.frame(
      xs_coef[paste0(vars[1], v1_comp, ":", vars[2]), stats, drop = FALSE],
      stringsAsFactors = FALSE
    )
    colnames(x_stats) <- names(stats)
    x_stats$term <- v1_comp
    x_numbers <- table(fit_glm$data[[vars[1]]])
    x_stats$term_label <- h_simple_term_labels(v1_comp, x_numbers)
    v1_ref <- xs_level[[vars[1]]][1]
    term_main <- v1_ref
    ref_label <- h_simple_term_labels(v1_ref, x_numbers)
  } else if (xs_class[vars[2]] != "numeric") {
    v2_comp <- xs_level[[vars[2]]][-1]
    v1_v2_grid <- expand.grid(v1 = v1_comp, v2 = v2_comp)
    x_sel <- paste(
      paste0(vars[1], v1_v2_grid$v1),
      paste0(vars[2], v1_v2_grid$v2),
      sep = ":"
    )
    x_stats <- as.data.frame(xs_coef[x_sel, stats, drop = FALSE], stringsAsFactors = FALSE)
    colnames(x_stats) <- names(stats)
    x_stats$term <- paste(v1_v2_grid$v1, "*", v1_v2_grid$v2)
    x_numbers <- table(fit_glm$data[[vars[1]]], fit_glm$data[[vars[2]]])
    x_stats$term_label <- h_interaction_term_labels(v1_v2_grid$v1, v1_v2_grid$v2, x_numbers)
    v1_ref <- xs_level[[vars[1]]][1]
    v2_ref <- xs_level[[vars[2]]][1]
    term_main <- paste(vars[1], vars[2], sep = " * ")
    ref_label <- h_interaction_term_labels(v1_ref, v2_ref, x_numbers, any = TRUE)
  }
  x_stats$df <- as.list(1)
  x_stats$pvalue <- as.list(x_stats$pvalue)
  x_stats$is_variable_summary <- FALSE
  x_stats$is_term_summary <- TRUE
  x_main <- data.frame(
    pvalue = main_effects[x, "Pr(>Chisq)", drop = TRUE],
    term = term_main,
    term_label = paste("Reference", ref_label),
    df = main_effects[x, "Df", drop = TRUE],
    stringsAsFactors = FALSE
  )
  x_main$pvalue <- as.list(x_main$pvalue)
  x_main$df <- as.list(x_main$df)
  x_main$estimate <- list(numeric(0))
  x_main$std_error <- list(numeric(0))
  x_main$is_variable_summary <- TRUE
  x_main$is_term_summary <- FALSE

  x_stats <- rbind(x_main, x_stats)
  x_stats$variable <- x
  x_stats$variable_label <- paste(
    "Interaction of",
    formatters::var_labels(fit_glm$data[vars[1]], fill = TRUE),
    "*",
    formatters::var_labels(fit_glm$data[vars[2]], fill = TRUE)
  )
  x_stats$interaction <- ""
  x_stats$interaction_label <- ""
  x_stats$reference <- ""
  x_stats$reference_label <- ""
  rownames(x_stats) <- NULL
  x_stats[c(
    "variable",
    "variable_label",
    "term",
    "term_label",
    "interaction",
    "interaction_label",
    "reference",
    "reference_label",
    "estimate",
    "std_error",
    "df",
    "pvalue",
    "is_variable_summary",
    "is_term_summary"
  )]
}

#' @describeIn h_logistic_regression Helper function to tabulate the interaction
#'   results of a logistic regression model. This basically is a wrapper for
#'   [h_or_interaction()] and [h_glm_simple_term_extract()] which puts the results
#'   in the right data frame format.
#'
#' @return A `data.frame` of tabulated interaction term results from a logistic regression model.
#'
#' @examples
#' h_glm_inter_term_extract("AGE", "ARMCD", mod2)
#'
#' @export
h_glm_inter_term_extract <- function(odds_ratio_var,
                                     interaction_var,
                                     fit_glm,
                                     ...) {
  # First obtain the main effects.
  main_stats <- h_glm_simple_term_extract(odds_ratio_var, fit_glm)
  main_stats$is_reference_summary <- FALSE
  main_stats$odds_ratio <- NA
  main_stats$lcl <- NA
  main_stats$ucl <- NA

  # Then we get the odds ratio estimates and put into df form.
  or_numbers <- h_or_interaction(odds_ratio_var, interaction_var, fit_glm, ...)
  is_num_or_var <- attr(fit_glm$terms, "dataClasses")[odds_ratio_var] == "numeric"

  if (is_num_or_var) {
    # Numeric OR variable case.
    references <- names(or_numbers)
    n_ref <- length(references)

    extract_from_list <- function(l, name, pos = 1) {
      unname(unlist(
        lapply(or_numbers, function(x) {
          x[[name]][pos]
        })
      ))
    }
    or_stats <- data.frame(
      variable = odds_ratio_var,
      variable_label = unname(formatters::var_labels(fit_glm$data[odds_ratio_var], fill = TRUE)),
      term = odds_ratio_var,
      term_label = unname(formatters::var_labels(fit_glm$data[odds_ratio_var], fill = TRUE)),
      interaction = interaction_var,
      interaction_label = unname(formatters::var_labels(fit_glm$data[interaction_var], fill = TRUE)),
      reference = references,
      reference_label = references,
      estimate = NA,
      std_error = NA,
      odds_ratio = extract_from_list(or_numbers, "or"),
      lcl = extract_from_list(or_numbers, "ci", pos = "lcl"),
      ucl = extract_from_list(or_numbers, "ci", pos = "ucl"),
      df = NA,
      pvalue = NA,
      is_variable_summary = FALSE,
      is_term_summary = FALSE,
      is_reference_summary = TRUE
    )
  } else {
    # Categorical OR variable case.
    references <- names(or_numbers[[1]])
    n_ref <- length(references)

    extract_from_list <- function(l, name, pos = 1) {
      unname(unlist(
        lapply(or_numbers, function(x) {
          lapply(x, function(y) y[[name]][pos])
        })
      ))
    }
    or_stats <- data.frame(
      variable = odds_ratio_var,
      variable_label = unname(formatters::var_labels(fit_glm$data[odds_ratio_var], fill = TRUE)),
      term = rep(names(or_numbers), each = n_ref),
      term_label = h_simple_term_labels(rep(names(or_numbers), each = n_ref), table(fit_glm$data[[odds_ratio_var]])),
      interaction = interaction_var,
      interaction_label = unname(formatters::var_labels(fit_glm$data[interaction_var], fill = TRUE)),
      reference = unlist(lapply(or_numbers, names)),
      reference_label = unlist(lapply(or_numbers, names)),
      estimate = NA,
      std_error = NA,
      odds_ratio = extract_from_list(or_numbers, "or"),
      lcl = extract_from_list(or_numbers, "ci", pos = "lcl"),
      ucl = extract_from_list(or_numbers, "ci", pos = "ucl"),
      df = NA,
      pvalue = NA,
      is_variable_summary = FALSE,
      is_term_summary = FALSE,
      is_reference_summary = TRUE
    )
  }

  df <- rbind(
    main_stats[, names(or_stats)],
    or_stats
  )
  df[order(-df$is_variable_summary, df$term, -df$is_term_summary, df$reference), ]
}

#' @describeIn h_logistic_regression Helper function to tabulate the results including
#'   odds ratios and confidence intervals of simple terms.
#'
#' @return Tabulated statistics for the given variable(s) from the logistic regression model.
#'
#' @examples
#' h_logistic_simple_terms("AGE", mod1)
#'
#' @export
h_logistic_simple_terms <- function(x, fit_glm, conf_level = 0.95) {
  checkmate::assert_multi_class(fit_glm, c("glm", "clogit"))
  if (inherits(fit_glm, "glm")) {
    checkmate::assert_set_equal(fit_glm$family$family, "binomial")
  }
  terms_name <- attr(stats::terms(fit_glm), "term.labels")
  xs_class <- attr(fit_glm$terms, "dataClasses")
  interaction <- terms_name[which(!terms_name %in% names(xs_class))]
  checkmate::assert_subset(x, terms_name)
  if (length(interaction) != 0) {
    # Make sure any item in x is not part of interaction term
    checkmate::assert_disjunct(x, unlist(strsplit(interaction, ":")))
  }
  x_stats <- lapply(x, h_glm_simple_term_extract, fit_glm)
  x_stats <- do.call(rbind, x_stats)
  q_norm <- stats::qnorm((1 + conf_level) / 2)
  x_stats$odds_ratio <- lapply(x_stats$estimate, exp)
  x_stats$lcl <- Map(function(or, se) exp(log(or) - q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  x_stats$ucl <- Map(function(or, se) exp(log(or) + q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  x_stats$ci <- Map(function(lcl, ucl) c(lcl, ucl), lcl = x_stats$lcl, ucl = x_stats$ucl)
  x_stats
}

#' @describeIn h_logistic_regression Helper function to tabulate the results including
#'   odds ratios and confidence intervals of interaction terms.
#'
#' @return Tabulated statistics for the given variable(s) from the logistic regression model.
#'
#' @examples
#' h_logistic_inter_terms(c("RACE", "AGE", "ARMCD", "AGE:ARMCD"), mod2)
#'
#' @export
h_logistic_inter_terms <- function(x,
                                   fit_glm,
                                   conf_level = 0.95,
                                   at = NULL) {
  # Find out the interaction variables and interaction term.
  inter_vars <- h_get_interaction_vars(fit_glm)
  checkmate::assert_vector(inter_vars, len = 2)


  inter_term_index <- intersect(grep(inter_vars[1], x), grep(inter_vars[2], x))
  inter_term <- x[inter_term_index]

  # For the non-interaction vars we need the standard stuff.
  normal_terms <- setdiff(x, union(inter_vars, inter_term))

  x_stats <- lapply(normal_terms, h_glm_simple_term_extract, fit_glm)
  x_stats <- do.call(rbind, x_stats)
  q_norm <- stats::qnorm((1 + conf_level) / 2)
  x_stats$odds_ratio <- lapply(x_stats$estimate, exp)
  x_stats$lcl <- Map(function(or, se) exp(log(or) - q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  x_stats$ucl <- Map(function(or, se) exp(log(or) + q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  normal_stats <- x_stats
  normal_stats$is_reference_summary <- FALSE

  # Now the interaction term itself.
  inter_term_stats <- h_glm_interaction_extract(inter_term, fit_glm)
  inter_term_stats$odds_ratio <- NA
  inter_term_stats$lcl <- NA
  inter_term_stats$ucl <- NA
  inter_term_stats$is_reference_summary <- FALSE

  is_intervar1_numeric <- attr(fit_glm$terms, "dataClasses")[inter_vars[1]] == "numeric"

  # Interaction stuff.
  inter_stats_one <- h_glm_inter_term_extract(
    inter_vars[1],
    inter_vars[2],
    fit_glm,
    conf_level = conf_level,
    at = `if`(is_intervar1_numeric, NULL, at)
  )
  inter_stats_two <- h_glm_inter_term_extract(
    inter_vars[2],
    inter_vars[1],
    fit_glm,
    conf_level = conf_level,
    at = `if`(is_intervar1_numeric, at, NULL)
  )

  # Now just combine everything in one data frame.
  col_names <- c(
    "variable",
    "variable_label",
    "term",
    "term_label",
    "interaction",
    "interaction_label",
    "reference",
    "reference_label",
    "estimate",
    "std_error",
    "df",
    "pvalue",
    "odds_ratio",
    "lcl",
    "ucl",
    "is_variable_summary",
    "is_term_summary",
    "is_reference_summary"
  )
  df <- rbind(
    inter_stats_one[, col_names],
    inter_stats_two[, col_names],
    inter_term_stats[, col_names]
  )
  if (length(normal_terms) > 0) {
    df <- rbind(
      normal_stats[, col_names],
      df
    )
  }
  df$ci <- combine_vectors(df$lcl, df$ucl)
  df
}

#' Tabulate Survival Duration by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Tabulate statistics such as median survival time and hazard ratio for population subgroups.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_coxph_pairwise
#' @param df (`list`)\cr of data frames containing all analysis variables. List should be
#'   created using [extract_survival_subgroups()].
#' @param vars (`character`)\cr the name of statistics to be reported among:
#'   * `n_tot_events`: Total number of events per group.
#'   * `n_events`: Number of events per group.
#'   * `n_tot`: Total number of observations per group.
#'   * `n`: Number of observations per group.
#'   * `median`: Median survival time.
#'   * `hr`: Hazard ratio.
#'   * `ci`: Confidence interval of hazard ratio.
#'   * `pval`: p-value of the effect.
#'   Note, one of the statistics `n_tot` and `n_tot_events`, as well as both `hr` and `ci`
#'   are required.
#' @param time_unit (`string`)\cr label with unit of median survival time. Default `NULL` skips displaying unit.
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. Tables typically used as part of forest plot.
#'
#' @seealso [extract_survival_subgroups()]
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte)
#'
#' adtte_f <- adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X"),
#'     SEX %in% c("M", "F")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
#'     SEX = droplevels(SEX),
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- c(
#'   "ARM" = adtte_labels[["ARM"]],
#'   "SEX" = adtte_labels[["SEX"]],
#'   "AVALU" = adtte_labels[["AVALU"]],
#'   "is_event" = "Event Flag"
#' )
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' df <- extract_survival_subgroups(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM", subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f
#' )
#' df
#'
#' df_grouped <- extract_survival_subgroups(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM", subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#' df_grouped
#'
#' @name survival_duration_subgroups
#' @order 1
NULL

#' Prepares Survival Data for Population Subgroups in Data Frames
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares estimates of median survival times and treatment hazard ratios for population subgroups in
#' data frames. Simple wrapper for [h_survtime_subgroups_df()] and [h_coxph_subgroups_df()]. Result is a `list`
#' of two `data.frame`s: `survtime` and `hr`. `variables` corresponds to the names of variables found in `data`,
#' passed as a named `list` and requires elements `tte`, `is_event`, `arm` and optionally `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_duration_subgroups
#' @inheritParams survival_coxph_pairwise
#'
#' @return A named `list` of two elements:
#'   * `survtime`: A `data.frame` containing columns `arm`, `n`, `n_events`, `median`, `subgroup`, `var`,
#'     `var_label`, and `row_type`.
#'   * `hr`: A `data.frame` containing columns `arm`, `n_tot`, `n_tot_events`, `hr`, `lcl`, `ucl`, `conf_level`,
#'     `pval`, `pval_label`, `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @seealso [survival_duration_subgroups]
#'
#' @export
extract_survival_subgroups <- function(variables,
                                       data,
                                       groups_lists = list(),
                                       control = control_coxph(),
                                       label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_survival_subgroups() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  df_survtime <- h_survtime_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    label_all = label_all
  )
  df_hr <- h_coxph_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    control = control,
    label_all = label_all
  )

  list(survtime = df_survtime, hr = df_hr)
}

#' @describeIn survival_duration_subgroups  Formatted analysis function which is used as
#'   `afun` in `tabulate_survival_subgroups()`.
#'
#' @return
#' * `a_survival_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_survival_subgroups <- function(.formats = list( # nolint start
                                   n = "xx",
                                   n_events = "xx",
                                   n_tot_events = "xx",
                                   median = "xx.x",
                                   n_tot = "xx",
                                   hr = list(format_extreme_values(2L)),
                                   ci = list(format_extreme_values_ci(2L)),
                                   pval = "x.xxxx | (<0.0001)"
                                 ),
                                 na_str = default_na_str()) { # nolint end
  checkmate::assert_list(.formats)
  checkmate::assert_subset(
    names(.formats),
    c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval")
  )

  afun_lst <- Map(
    function(stat, fmt, na_str) {
      if (stat == "ci") {
        function(df, labelstr = "", ...) {
          in_rows(
            .list = combine_vectors(df$lcl, df$ucl),
            .labels = as.character(df$subgroup),
            .formats = fmt,
            .format_na_strs = na_str
          )
        }
      } else {
        function(df, labelstr = "", ...) {
          in_rows(
            .list = as.list(df[[stat]]),
            .labels = as.character(df$subgroup),
            .formats = fmt,
            .format_na_strs = na_str
          )
        }
      }
    },
    stat = names(.formats),
    fmt = .formats,
    na_str = na_str
  )

  afun_lst
}

#' @describeIn survival_duration_subgroups Table-creating function which creates a table
#'   summarizing survival by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
#'   and [rtables::summarize_row_groups()].
#'
#' @return An `rtables` table summarizing survival by subgroup.
#'
#' @examples
#' ## Table with default columns.
#' basic_table() %>%
#'   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
#'
#' ## Table with a manually chosen set of columns: adding "pval".
#' basic_table() %>%
#'   tabulate_survival_subgroups(
#'     df = df,
#'     vars = c("n_tot_events", "n_events", "median", "hr", "ci", "pval"),
#'     time_unit = adtte_f$AVALU[1]
#'   )
#'
#' @export
#' @order 2
tabulate_survival_subgroups <- function(lyt,
                                        df,
                                        vars = c("n_tot_events", "n_events", "median", "hr", "ci"),
                                        groups_lists = list(),
                                        label_all = "All Patients",
                                        time_unit = NULL,
                                        na_str = default_na_str()) {
  conf_level <- df$hr$conf_level[1]
  method <- df$hr$pval_label[1]

  extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method, label_all = label_all)

  afun_lst <- a_survival_subgroups(na_str = na_str)
  colvars <- d_survival_subgroups_colvars(
    vars,
    conf_level = conf_level,
    method = method,
    time_unit = time_unit
  )

  colvars_survtime <- list(
    vars = colvars$vars[names(colvars$labels) %in% c("n", "n_events", "median")],
    labels = colvars$labels[names(colvars$labels) %in% c("n", "n_events", "median")]
  )
  colvars_hr <- list(
    vars = colvars$vars[names(colvars$labels) %in% c("n_tot", "n_tot_events", "hr", "ci", "pval")],
    labels = colvars$labels[names(colvars$labels) %in% c("n_tot", "n_tot_events", "hr", "ci", "pval")]
  )

  # Columns from table_survtime are optional.
  if (length(colvars_survtime$vars) > 0) {
    lyt_survtime <- split_cols_by(lyt = lyt, var = "arm")
    lyt_survtime <- split_rows_by(
      lyt = lyt_survtime,
      var = "row_type",
      split_fun = keep_split_levels("content"),
      nested = FALSE
    )
    lyt_survtime <- summarize_row_groups(
      lyt = lyt_survtime,
      var = "var_label",
      cfun = afun_lst[names(colvars_survtime$labels)],
      na_str = na_str,
      extra_args = extra_args
    )
    lyt_survtime <- split_cols_by_multivar(
      lyt = lyt_survtime,
      vars = colvars_survtime$vars,
      varlabels = colvars_survtime$labels
    )

    if ("analysis" %in% df$survtime$row_type) {
      lyt_survtime <- split_rows_by(
        lyt = lyt_survtime,
        var = "row_type",
        split_fun = keep_split_levels("analysis"),
        nested = FALSE,
        child_labels = "hidden"
      )
      lyt_survtime <- split_rows_by(lyt = lyt_survtime, var = "var_label", nested = TRUE)
      lyt_survtime <- analyze_colvars(
        lyt = lyt_survtime,
        afun = afun_lst[names(colvars_survtime$labels)],
        na_str = na_str,
        inclNAs = TRUE,
        extra_args = extra_args
      )
    }

    table_survtime <- build_table(lyt_survtime, df = df$survtime)
  } else {
    table_survtime <- NULL
  }

  # Columns "n_tot_events" or "n_tot", and "hr", "ci" in table_hr are required.
  lyt_hr <- split_cols_by(lyt = lyt, var = "arm")
  lyt_hr <- split_rows_by(
    lyt = lyt_hr,
    var = "row_type",
    split_fun = keep_split_levels("content"),
    nested = FALSE
  )
  lyt_hr <- summarize_row_groups(
    lyt = lyt_hr,
    var = "var_label",
    cfun = afun_lst[names(colvars_hr$labels)],
    na_str = na_str,
    extra_args = extra_args
  )
  lyt_hr <- split_cols_by_multivar(
    lyt = lyt_hr,
    vars = colvars_hr$vars,
    varlabels = colvars_hr$labels
  ) %>%
    append_topleft("Baseline Risk Factors")

  if ("analysis" %in% df$survtime$row_type) {
    lyt_hr <- split_rows_by(
      lyt = lyt_hr,
      var = "row_type",
      split_fun = keep_split_levels("analysis"),
      nested = FALSE,
      child_labels = "hidden"
    )
    lyt_hr <- split_rows_by(lyt = lyt_hr, var = "var_label", nested = TRUE)
    lyt_hr <- analyze_colvars(
      lyt = lyt_hr,
      afun = afun_lst[names(colvars_hr$labels)],
      na_str = na_str,
      inclNAs = TRUE,
      extra_args = extra_args
    )
  }
  table_hr <- build_table(lyt_hr, df = df$hr)

  # There can be one or two vars starting with "n_tot".
  n_tot_ids <- grep("^n_tot", colvars_hr$vars)
  if (is.null(table_survtime)) {
    result <- table_hr
    hr_id <- match("hr", colvars_hr$vars)
    ci_id <- match("lcl", colvars_hr$vars)
  } else {
    # Reorder the table.
    result <- cbind_rtables(table_hr[, n_tot_ids], table_survtime, table_hr[, -n_tot_ids])
    # And then calculate column indices accordingly.
    hr_id <- length(n_tot_ids) + ncol(table_survtime) + match("hr", colvars_hr$vars[-n_tot_ids])
    ci_id <- length(n_tot_ids) + ncol(table_survtime) + match("lcl", colvars_hr$vars[-n_tot_ids])
    n_tot_ids <- seq_along(n_tot_ids)
  }

  structure(
    result,
    forest_header = paste0(rev(levels(df$survtime$arm)), "\nBetter"),
    col_x = hr_id,
    col_ci = ci_id,
    # Take the first one for scaling the symbol sizes in graph.
    col_symbol_size = n_tot_ids[1]
  )
}

#' Labels for Column Variables in Survival Duration by Subgroup Table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Internal function to check variables included in [tabulate_survival_subgroups()] and create column labels.
#'
#' @inheritParams tabulate_survival_subgroups
#' @inheritParams argument_convention
#' @param method (`character`)\cr p-value method for testing hazard ratio = 1.
#'
#' @return A `list` of variables and their labels to tabulate.
#'
#' @note At least one of `n_tot` and `n_tot_events` must be provided in `vars`.
#'
#' @export
d_survival_subgroups_colvars <- function(vars,
                                         conf_level,
                                         method,
                                         time_unit = NULL) {
  checkmate::assert_character(vars)
  checkmate::assert_string(time_unit, null.ok = TRUE)
  checkmate::assert_subset(c("hr", "ci"), vars)
  checkmate::assert_true(any(c("n_tot", "n_tot_events") %in% vars))
  checkmate::assert_subset(
    vars,
    c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval")
  )

  propcase_time_label <- if (!is.null(time_unit)) {
    paste0("Median (", time_unit, ")")
  } else {
    "Median"
  }

  varlabels <- c(
    n = "n",
    n_events = "Events",
    median = propcase_time_label,
    n_tot = "Total n",
    n_tot_events = "Total Events",
    hr = "Hazard Ratio",
    ci = paste0(100 * conf_level, "% Wald CI"),
    pval = method
  )

  colvars <- vars

  # The `lcl` variable is just a placeholder available in the analysis data,
  # it is not acutally used in the tabulation.
  # Variables used in the tabulation are lcl and ucl, see `a_survival_subgroups` for details.
  colvars[colvars == "ci"] <- "lcl"

  list(
    vars = colvars,
    labels = varlabels[vars]
  )
}

#' Summary numeric variables in columns
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Layout-creating function which can be used for creating column-wise summary tables.
#' This function sets the analysis methods as column labels and is a wrapper for
#' [rtables::analyze_colvars()]. It was designed principally for PK tables.
#'
#' @inheritParams argument_convention
#' @inheritParams rtables::analyze_colvars
#' @param imp_rule (`character`)\cr imputation rule setting. Defaults to `NULL` for no imputation rule. Can
#'   also be `"1/3"` to implement 1/3 imputation rule or `"1/2"` to implement 1/2 imputation rule. In order
#'   to use an imputation rule, the `avalcat_var` argument must be specified. See [imputation_rule()]
#'   for more details on imputation.
#' @param avalcat_var (`character`)\cr if `imp_rule` is not `NULL`, name of variable that indicates whether a
#'   row in the data corresponds to an analysis value in category `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or none of
#'   the above (defaults to `"AVALCAT1"`). Variable must be present in the data and should match the variable
#'   used to calculate the `n_blq` statistic (if included in `.stats`).
#' @param cache (`flag`)\cr whether to store computed values in a temporary caching environment. This will
#'   speed up calculations in large tables, but should be set to `FALSE` if the same `rtable` layout is
#'   used for multiple tables with different data. Defaults to `FALSE`.
#' @param row_labels (`character`)\cr as this function works in columns space, usual `.labels`
#'   character vector applies on the column space. You can change the row labels by defining this
#'   parameter to a named character vector with names corresponding to the split values. It defaults
#'   to `NULL` and if it contains only one `string`, it will duplicate that as a row label.
#' @param do_summarize_row_groups (`flag`)\cr defaults to `FALSE` and applies the analysis to the current
#'   label rows. This is a wrapper of [rtables::summarize_row_groups()] and it can accept `labelstr`
#'   to define row labels. This behavior is not supported as we never need to overload row labels.
#' @param split_col_vars (`flag`)\cr defaults to `TRUE` and puts the analysis results onto the columns.
#'   This option allows you to add multiple instances of this functions, also in a nested fashion,
#'   without adding more splits. This split must happen only one time on a single layout.
#'
#' @return
#' A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
#' Adding this function to an `rtable` layout will summarize the given variables, arrange the output
#' in columns, and add it to the table layout.
#'
#' @note This is an experimental implementation of [rtables::summarize_row_groups()] and
#'   [rtables::analyze_colvars()] that may be subjected to changes as `rtables` extends its
#'   support to more complex analysis pipelines on the column space. For the same reasons,
#'   we encourage to read the examples carefully and file issues for cases that differ from
#'   them.
#'
#'   Here `labelstr` behaves differently than usual. If it is not defined (default as `NULL`),
#'   row labels are assigned automatically to the split values in case of `rtables::analyze_colvars`
#'   (`do_summarize_row_groups = FALSE`, the default), and to the group label for
#'   `do_summarize_row_groups = TRUE`.
#'
#' @seealso [analyze_vars()], [rtables::analyze_colvars()].
#'
#' @examples
#' library(dplyr)
#'
#' # Data preparation
#' adpp <- tern_ex_adpp %>% h_pkparam_sort()
#'
#' lyt <- basic_table() %>%
#'   split_rows_by(var = "STRATA1", label_pos = "topleft") %>%
#'   split_rows_by(
#'     var = "SEX",
#'     label_pos = "topleft",
#'     child_labels = "hidden"
#'   ) %>% # Removes duplicated labels
#'   analyze_vars_in_cols(vars = "AGE")
#' result <- build_table(lyt = lyt, df = adpp)
#' result
#'
#' # By selecting just some statistics and ad-hoc labels
#' lyt <- basic_table() %>%
#'   split_rows_by(var = "ARM", label_pos = "topleft") %>%
#'   split_rows_by(
#'     var = "SEX",
#'     label_pos = "topleft",
#'     child_labels = "hidden",
#'     split_fun = drop_split_levels
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AGE",
#'     .stats = c("n", "cv", "geom_mean"),
#'     .labels = c(
#'       n = "aN",
#'       cv = "aCV",
#'       geom_mean = "aGeomMean"
#'     )
#'   )
#' result <- build_table(lyt = lyt, df = adpp)
#' result
#'
#' # Changing row labels
#' lyt <- basic_table() %>%
#'   analyze_vars_in_cols(
#'     vars = "AGE",
#'     row_labels = "some custom label"
#'   )
#' result <- build_table(lyt, df = adpp)
#' result
#'
#' # Pharmacokinetic parameters
#' lyt <- basic_table() %>%
#'   split_rows_by(
#'     var = "TLG_DISPLAY",
#'     split_label = "PK Parameter",
#'     label_pos = "topleft",
#'     child_labels = "hidden"
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AVAL"
#'   )
#' result <- build_table(lyt, df = adpp)
#' result
#'
#' # Multiple calls (summarize label and analyze underneath)
#' lyt <- basic_table() %>%
#'   split_rows_by(
#'     var = "TLG_DISPLAY",
#'     split_label = "PK Parameter",
#'     label_pos = "topleft"
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AVAL",
#'     do_summarize_row_groups = TRUE # does a summarize level
#'   ) %>%
#'   split_rows_by("SEX",
#'     child_labels = "hidden",
#'     label_pos = "topleft"
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AVAL",
#'     split_col_vars = FALSE # avoids re-splitting the columns
#'   )
#' result <- build_table(lyt, df = adpp)
#' result
#'
#' @export
analyze_vars_in_cols <- function(lyt,
                                 vars,
                                 ...,
                                 .stats = c(
                                   "n",
                                   "mean",
                                   "sd",
                                   "se",
                                   "cv",
                                   "geom_cv"
                                 ),
                                 .labels = c(
                                   n = "n",
                                   mean = "Mean",
                                   sd = "SD",
                                   se = "SE",
                                   cv = "CV (%)",
                                   geom_cv = "CV % Geometric Mean"
                                 ),
                                 row_labels = NULL,
                                 do_summarize_row_groups = FALSE,
                                 split_col_vars = TRUE,
                                 imp_rule = NULL,
                                 avalcat_var = "AVALCAT1",
                                 cache = FALSE,
                                 .indent_mods = NULL,
                                 na_str = default_na_str(),
                                 nested = TRUE,
                                 .formats = NULL,
                                 .aligns = NULL) {
  extra_args <- list(...)

  checkmate::assert_string(na_str, na.ok = TRUE, null.ok = TRUE)
  checkmate::assert_character(row_labels, null.ok = TRUE)
  checkmate::assert_int(.indent_mods, null.ok = TRUE)
  checkmate::assert_flag(nested)
  checkmate::assert_flag(split_col_vars)
  checkmate::assert_flag(do_summarize_row_groups)

  # Filtering
  met_grps <- paste0("analyze_vars", c("_numeric", "_counts"))
  .stats <- get_stats(met_grps, stats_in = .stats)
  formats_v <- get_formats_from_stats(stats = .stats, formats_in = .formats)
  labels_v <- get_labels_from_stats(stats = .stats, labels_in = .labels)
  if ("control" %in% names(extra_args)) labels_v <- labels_v %>% labels_use_control(extra_args[["control"]], .labels)

  # Check for vars in the case that one or more are used
  if (length(vars) == 1) {
    vars <- rep(vars, length(.stats))
  } else if (length(vars) != length(.stats)) {
    stop(
      "Analyzed variables (vars) does not have the same ",
      "number of elements of specified statistics (.stats)."
    )
  }

  if (split_col_vars) {
    # Checking there is not a previous identical column split
    clyt <- tail(clayout(lyt), 1)[[1]]

    dummy_lyt <- split_cols_by_multivar(
      lyt = basic_table(),
      vars = vars,
      varlabels = labels_v
    )

    if (any(sapply(clyt, identical, y = get_last_col_split(dummy_lyt)))) {
      stop(
        "Column split called again with the same values. ",
        "This can create many unwanted columns. Please consider adding ",
        "split_col_vars = FALSE to the last call of ",
        deparse(sys.calls()[[sys.nframe() - 1]]), "."
      )
    }

    # Main col split
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = vars,
      varlabels = labels_v
    )
  }

  env <- new.env() # create caching environment

  if (do_summarize_row_groups) {
    if (length(unique(vars)) > 1) {
      stop("When using do_summarize_row_groups only one label level var should be inserted.")
    }

    # Function list for do_summarize_row_groups. Slightly different handling of labels
    cfun_list <- Map(
      function(stat, use_cache, cache_env) {
        function(u, .spl_context, labelstr, .df_row, ...) {
          # Statistic
          var_row_val <- paste(
            gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
            paste(.spl_context$value, collapse = "_"),
            sep = "_"
          )
          if (use_cache) {
            if (is.null(cache_env[[var_row_val]])) cache_env[[var_row_val]] <- s_summary(u, ...)
            x_stats <- cache_env[[var_row_val]]
          } else {
            x_stats <- s_summary(u, ...)
          }

          if (is.null(imp_rule) || !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
            res <- x_stats[[stat]]
          } else {
            timept <- as.numeric(gsub(".*?([0-9\\.]+).*", "\\1", tail(.spl_context$value, 1)))
            res_imp <- imputation_rule(
              .df_row, x_stats, stat,
              imp_rule = imp_rule,
              post = grepl("Predose", tail(.spl_context$value, 1)) || timept > 0,
              avalcat_var = avalcat_var
            )
            res <- res_imp[["val"]]
            na_str <- res_imp[["na_str"]]
          }

          # Label check and replacement
          if (length(row_labels) > 1) {
            if (!(labelstr %in% names(row_labels))) {
              stop(
                "Replacing the labels in do_summarize_row_groups needs a named vector",
                "that contains the split values. In the current split variable ",
                .spl_context$split[nrow(.spl_context)],
                " the labelstr value (split value by default) ", labelstr, " is not in",
                " row_labels names: ", names(row_labels)
              )
            }
            lbl <- unlist(row_labels[labelstr])
          } else {
            lbl <- labelstr
          }

          # Cell creation
          rcell(res,
            label = lbl,
            format = formats_v[names(formats_v) == stat][[1]],
            format_na_str = na_str,
            indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
            align = .aligns
          )
        }
      },
      stat = .stats,
      use_cache = cache,
      cache_env = replicate(length(.stats), env)
    )

    # Main call to rtables
    summarize_row_groups(
      lyt = lyt,
      var = unique(vars),
      cfun = cfun_list,
      na_str = na_str,
      extra_args = extra_args
    )
  } else {
    # Function list for analyze_colvars
    afun_list <- Map(
      function(stat, use_cache, cache_env) {
        function(u, .spl_context, .df_row, ...) {
          # Main statistics
          var_row_val <- paste(
            gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
            paste(.spl_context$value, collapse = "_"),
            sep = "_"
          )
          if (use_cache) {
            if (is.null(cache_env[[var_row_val]])) cache_env[[var_row_val]] <- s_summary(u, ...)
            x_stats <- cache_env[[var_row_val]]
          } else {
            x_stats <- s_summary(u, ...)
          }

          if (is.null(imp_rule) || !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
            res <- x_stats[[stat]]
          } else {
            timept <- as.numeric(gsub(".*?([0-9\\.]+).*", "\\1", tail(.spl_context$value, 1)))
            res_imp <- imputation_rule(
              .df_row, x_stats, stat,
              imp_rule = imp_rule,
              post = grepl("Predose", tail(.spl_context$value, 1)) || timept > 0,
              avalcat_var = avalcat_var
            )
            res <- res_imp[["val"]]
            na_str <- res_imp[["na_str"]]
          }

          if (is.list(res)) {
            if (length(res) > 1) {
              stop("The analyzed column produced more than one category of results.")
            } else {
              res <- unlist(res)
            }
          }

          # Label from context
          label_from_context <- .spl_context$value[nrow(.spl_context)]

          # Label switcher
          if (is.null(row_labels)) {
            lbl <- label_from_context
          } else {
            if (length(row_labels) > 1) {
              if (!(label_from_context %in% names(row_labels))) {
                stop(
                  "Replacing the labels in do_summarize_row_groups needs a named vector",
                  "that contains the split values. In the current split variable ",
                  .spl_context$split[nrow(.spl_context)],
                  " the split value ", label_from_context, " is not in",
                  " row_labels names: ", names(row_labels)
                )
              }
              lbl <- unlist(row_labels[label_from_context])
            } else {
              lbl <- row_labels
            }
          }

          # Cell creation
          rcell(res,
            label = lbl,
            format = formats_v[names(formats_v) == stat][[1]],
            format_na_str = na_str,
            indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
            align = .aligns
          )
        }
      },
      stat = .stats,
      use_cache = cache,
      cache_env = replicate(length(.stats), env)
    )

    # Main call to rtables
    analyze_colvars(lyt,
      afun = afun_list,
      na_str = na_str,
      nested = nested,
      extra_args = extra_args
    )
  }
}

# Help function
get_last_col_split <- function(lyt) {
  tail(tail(clayout(lyt), 1)[[1]], 1)[[1]]
}

#' Helper Functions for Tabulating Survival Duration by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions that tabulate in a data frame statistics such as median survival
#' time and hazard ratio for population subgroups.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_coxph_pairwise
#' @inheritParams survival_duration_subgroups
#' @param arm (`factor`)\cr the treatment group variable.
#'
#' @details Main functionality is to prepare data for use in a layout-creating function.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte)
#'
#' adtte_f <- adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X"),
#'     SEX %in% c("M", "F")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
#'     SEX = droplevels(SEX),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("ARM" = adtte_labels[["ARM"]], "SEX" = adtte_labels[["SEX"]], "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' @name h_survival_duration_subgroups
NULL

#' @describeIn h_survival_duration_subgroups helper to prepare a data frame of median survival times by arm.
#'
#' @return
#' * `h_survtime_df()` returns a `data.frame` with columns `arm`, `n`, `n_events`, and `median`.
#'
#' @examples
#' # Extract median survival time for one group.
#' h_survtime_df(
#'   tte = adtte_f$AVAL,
#'   is_event = adtte_f$is_event,
#'   arm = adtte_f$ARM
#' )
#'
#' @export
h_survtime_df <- function(tte, is_event, arm) {
  checkmate::assert_numeric(tte)
  checkmate::assert_logical(is_event, len = length(tte))
  assert_valid_factor(arm, len = length(tte))

  df_tte <- data.frame(
    tte = tte,
    is_event = is_event,
    stringsAsFactors = FALSE
  )

  # Delete NAs
  non_missing_rows <- stats::complete.cases(df_tte)
  df_tte <- df_tte[non_missing_rows, ]
  arm <- arm[non_missing_rows]

  lst_tte <- split(df_tte, arm)
  lst_results <- Map(function(x, arm) {
    if (nrow(x) > 0) {
      s_surv <- s_surv_time(x, .var = "tte", is_event = "is_event")
      median_est <- unname(as.numeric(s_surv$median))
      n_events <- sum(x$is_event)
    } else {
      median_est <- NA
      n_events <- NA
    }

    data.frame(
      arm = arm,
      n = nrow(x),
      n_events = n_events,
      median = median_est,
      stringsAsFactors = FALSE
    )
  }, lst_tte, names(lst_tte))

  df <- do.call(rbind, args = c(lst_results, make.row.names = FALSE))
  df$arm <- factor(df$arm, levels = levels(arm))
  df
}

#' @describeIn h_survival_duration_subgroups summarizes median survival times by arm and across subgroups
#'    in a data frame. `variables` corresponds to the names of variables found in `data`, passed as a named list and
#'    requires elements `tte`, `is_event`, `arm` and optionally `subgroups`. `groups_lists` optionally specifies
#'    groupings for `subgroups` variables.
#'
#' @return
#' * `h_survtime_subgroups_df()` returns a `data.frame` with columns `arm`, `n`, `n_events`, `median`, `subgroup`,
#'   `var`, `var_label`, and `row_type`.
#'
#' @examples
#' # Extract median survival time for multiple groups.
#' h_survtime_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f
#' )
#'
#' # Define groupings for BMRKR2 levels.
#' h_survtime_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' @export
h_survtime_subgroups_df <- function(variables,
                                    data,
                                    groups_lists = list(),
                                    label_all = "All Patients") {
  checkmate::assert_character(variables$tte)
  checkmate::assert_character(variables$is_event)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)

  assert_df_with_variables(data, variables)

  checkmate::assert_string(label_all)

  # Add All Patients.
  result_all <- h_survtime_df(data[[variables$tte]], data[[variables$is_event]], data[[variables$arm]])
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  # Add Subgroups.
  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
    l_result <- lapply(l_data, function(grp) {
      result <- h_survtime_df(grp$df[[variables$tte]], grp$df[[variables$is_event]], grp$df[[variables$arm]])
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"
    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' @describeIn h_survival_duration_subgroups helper to prepare a data frame with estimates of
#'   treatment hazard ratio.
#'
#' @param strata_data (`factor`, `data.frame` or `NULL`)\cr required if stratified analysis is performed.
#'
#' @return
#' * `h_coxph_df()` returns a `data.frame` with columns `arm`, `n_tot`, `n_tot_events`, `hr`, `lcl`, `ucl`,
#'   `conf_level`, `pval` and `pval_label`.
#'
#' @examples
#' # Extract hazard ratio for one group.
#' h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM)
#'
#' # Extract hazard ratio for one group with stratification factor.
#' h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1)
#'
#' @export
h_coxph_df <- function(tte, is_event, arm, strata_data = NULL, control = control_coxph()) {
  checkmate::assert_numeric(tte)
  checkmate::assert_logical(is_event, len = length(tte))
  assert_valid_factor(arm, n.levels = 2, len = length(tte))

  df_tte <- data.frame(tte = tte, is_event = is_event)
  strata_vars <- NULL

  if (!is.null(strata_data)) {
    if (is.data.frame(strata_data)) {
      strata_vars <- names(strata_data)
      checkmate::assert_data_frame(strata_data, nrows = nrow(df_tte))
      assert_df_with_factors(strata_data, as.list(stats::setNames(strata_vars, strata_vars)))
    } else {
      assert_valid_factor(strata_data, len = nrow(df_tte))
      strata_vars <- "strata_data"
    }
    df_tte[strata_vars] <- strata_data
  }

  l_df <- split(df_tte, arm)

  if (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) > 0) {
    # Hazard ratio and CI.
    result <- s_coxph_pairwise(
      df = l_df[[2]],
      .ref_group = l_df[[1]],
      .in_ref_col = FALSE,
      .var = "tte",
      is_event = "is_event",
      strata = strata_vars,
      control = control
    )

    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = unname(as.numeric(result$n_tot)),
      n_tot_events = unname(as.numeric(result$n_tot_events)),
      hr = unname(as.numeric(result$hr)),
      lcl = unname(result$hr_ci[1]),
      ucl = unname(result$hr_ci[2]),
      conf_level = control[["conf_level"]],
      pval = as.numeric(result$pvalue),
      pval_label = obj_label(result$pvalue),
      stringsAsFactors = FALSE
    )
  } else if (
    (nrow(l_df[[1]]) == 0 && nrow(l_df[[2]]) > 0) ||
      (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) == 0)
  ) {
    df_tte_complete <- df_tte[stats::complete.cases(df_tte), ]
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = nrow(df_tte_complete),
      n_tot_events = sum(df_tte_complete$is_event),
      hr = NA,
      lcl = NA,
      ucl = NA,
      conf_level = control[["conf_level"]],
      pval = NA,
      pval_label = NA,
      stringsAsFactors = FALSE
    )
  } else {
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = 0L,
      n_tot_events = 0L,
      hr = NA,
      lcl = NA,
      ucl = NA,
      conf_level = control[["conf_level"]],
      pval = NA,
      pval_label = NA,
      stringsAsFactors = FALSE
    )
  }

  df
}

#' @describeIn h_survival_duration_subgroups summarizes estimates of the treatment hazard ratio
#'   across subgroups in a data frame. `variables` corresponds to the names of variables found in
#'   `data`, passed as a named list and requires elements `tte`, `is_event`, `arm` and
#'   optionally `subgroups` and `strata`. `groups_lists` optionally specifies
#'   groupings for `subgroups` variables.
#'
#' @return
#' * `h_coxph_subgroups_df()` returns a `data.frame` with columns `arm`, `n_tot`, `n_tot_events`, `hr`,
#'   `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @examples
#' # Extract hazard ratio for multiple groups.
#' h_coxph_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f
#' )
#'
#' # Define groupings of BMRKR2 levels.
#' h_coxph_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' # Extract hazard ratio for multiple groups with stratification factors.
#' h_coxph_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2"),
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adtte_f
#' )
#'
#' @export
h_coxph_subgroups_df <- function(variables,
                                 data,
                                 groups_lists = list(),
                                 control = control_coxph(),
                                 label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_coxph_subgroups_df() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  checkmate::assert_character(variables$tte)
  checkmate::assert_character(variables$is_event)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_character(variables$strata, null.ok = TRUE)
  assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  assert_df_with_variables(data, variables)
  checkmate::assert_string(label_all)

  # Add All Patients.
  result_all <- h_coxph_df(
    tte = data[[variables$tte]],
    is_event = data[[variables$is_event]],
    arm = data[[variables$arm]],
    strata_data = if (is.null(variables$strata)) NULL else data[variables$strata],
    control = control
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  # Add Subgroups.
  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)

    l_result <- lapply(l_data, function(grp) {
      result <- h_coxph_df(
        tte = grp$df[[variables$tte]],
        is_event = grp$df[[variables$is_event]],
        arm = grp$df[[variables$arm]],
        strata_data = if (is.null(variables$strata)) NULL else grp$df[variables$strata],
        control = control
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })

    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"

    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' Split Dataframe by Subgroups
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Split a dataframe into a non-nested list of subsets.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_duration_subgroups
#' @param data (`data.frame`)\cr dataset to split.
#' @param subgroups (`character`)\cr names of factor variables from `data` used to create subsets.
#'   Unused levels not present in `data` are dropped. Note that the order in this vector
#'   determines the order in the downstream table.
#'
#' @return A list with subset data (`df`) and metadata about the subset (`df_labels`).
#'
#' @details Main functionality is to prepare data for use in forest plot layouts.
#'
#' @examples
#' df <- data.frame(
#'   x = c(1:5),
#'   y = factor(c("A", "B", "A", "B", "A"), levels = c("A", "B", "C")),
#'   z = factor(c("C", "C", "D", "D", "D"), levels = c("D", "C"))
#' )
#' formatters::var_labels(df) <- paste("label for", names(df))
#'
#' h_split_by_subgroups(
#'   data = df,
#'   subgroups = c("y", "z")
#' )
#'
#' h_split_by_subgroups(
#'   data = df,
#'   subgroups = c("y", "z"),
#'   groups_lists = list(
#'     y = list("AB" = c("A", "B"), "C" = "C")
#'   )
#' )
#'
#' @export
h_split_by_subgroups <- function(data,
                                 subgroups,
                                 groups_lists = list()) {
  checkmate::assert_character(subgroups, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(groups_lists, names = "named")
  checkmate::assert_subset(names(groups_lists), subgroups)
  assert_df_with_factors(data, as.list(stats::setNames(subgroups, subgroups)))

  data_labels <- unname(formatters::var_labels(data))
  df_subgroups <- data[, subgroups, drop = FALSE]
  subgroup_labels <- formatters::var_labels(df_subgroups, fill = TRUE)

  l_labels <- Map(function(grp_i, name_i) {
    existing_levels <- levels(droplevels(grp_i))
    grp_levels <- if (name_i %in% names(groups_lists)) {
      # For this variable groupings are defined. We check which groups are contained in the data.
      group_list_i <- groups_lists[[name_i]]
      group_has_levels <- vapply(group_list_i, function(lvls) any(lvls %in% existing_levels), TRUE)
      names(which(group_has_levels))
    } else {
      existing_levels
    }
    df_labels <- data.frame(
      subgroup = grp_levels,
      var = name_i,
      var_label = unname(subgroup_labels[name_i]),
      stringsAsFactors = FALSE # Rationale is that subgroups may not be unique.
    )
  }, df_subgroups, names(df_subgroups))

  # Create a dataframe with one row per subgroup.
  df_labels <- do.call(rbind, args = c(l_labels, make.row.names = FALSE))
  row_label <- paste0(df_labels$var, ".", df_labels$subgroup)
  row_split_var <- factor(row_label, levels = row_label)

  # Create a list of data subsets.
  lapply(split(df_labels, row_split_var), function(row_i) {
    which_row <- if (row_i$var %in% names(groups_lists)) {
      data[[row_i$var]] %in% groups_lists[[row_i$var]][[row_i$subgroup]]
    } else {
      data[[row_i$var]] == row_i$subgroup
    }
    df <- data[which_row, ]
    rownames(df) <- NULL
    formatters::var_labels(df) <- data_labels

    list(
      df = df,
      df_labels = data.frame(row_i, row.names = NULL)
    )
  })
}

#' Helper Function to create a map dataframe that can be used in `trim_levels_to_map` split function.
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper Function to create a map dataframe from the input dataset, which can be used as an argument in the
#' `trim_levels_to_map` split function. Based on different method, the map is constructed differently.
#'
#' @inheritParams argument_convention
#' @param abnormal (named `list`)\cr identifying the abnormal range level(s) in `df`. Based on the levels of
#'   abnormality of the input dataset, it can be something like `list(Low = "LOW LOW", High = "HIGH HIGH")` or
#'   `abnormal = list(Low = "LOW", High = "HIGH"))`
#' @param method (`string`)\cr indicates how the returned map will be constructed. Can be `"default"` or `"range"`.
#'
#' @return A map `data.frame`.
#'
#' @note If method is `"default"`, the returned map will only have the abnormal directions that are observed in the
#'   `df`, and records with all normal values will be excluded to avoid error in creating layout. If method is
#'   `"range"`, the returned map will be based on the rule that at least one observation with low range > 0
#'   for low direction and at least one observation with high range is not missing for high direction.
#'
#' @examples
#' adlb <- df_explicit_na(tern_ex_adlb)
#'
#' h_map_for_count_abnormal(
#'   df = adlb,
#'   variables = list(anl = "ANRIND", split_rows = c("LBCAT", "PARAM")),
#'   abnormal = list(low = c("LOW"), high = c("HIGH")),
#'   method = "default",
#'   na_str = "<Missing>"
#' )
#'
#' df <- data.frame(
#'   USUBJID = c(rep("1", 4), rep("2", 4), rep("3", 4)),
#'   AVISIT = c(
#'     rep("WEEK 1", 2),
#'     rep("WEEK 2", 2),
#'     rep("WEEK 1", 2),
#'     rep("WEEK 2", 2),
#'     rep("WEEK 1", 2),
#'     rep("WEEK 2", 2)
#'   ),
#'   PARAM = rep(c("ALT", "CPR"), 6),
#'   ANRIND = c(
#'     "NORMAL", "NORMAL", "LOW",
#'     "HIGH", "LOW", "LOW", "HIGH", "HIGH", rep("NORMAL", 4)
#'   ),
#'   ANRLO = rep(5, 12),
#'   ANRHI = rep(20, 12)
#' )
#' df$ANRIND <- factor(df$ANRIND, levels = c("LOW", "HIGH", "NORMAL"))
#' h_map_for_count_abnormal(
#'   df = df,
#'   variables = list(
#'     anl = "ANRIND",
#'     split_rows = c("PARAM"),
#'     range_low = "ANRLO",
#'     range_high = "ANRHI"
#'   ),
#'   abnormal = list(low = c("LOW"), high = c("HIGH")),
#'   method = "range",
#'   na_str = "<Missing>"
#' )
#'
#' @export
h_map_for_count_abnormal <- function(df,
                                     variables = list(
                                       anl = "ANRIND",
                                       split_rows = c("PARAM"),
                                       range_low = "ANRLO",
                                       range_high = "ANRHI"
                                     ),
                                     abnormal = list(low = c("LOW", "LOW LOW"), high = c("HIGH", "HIGH HIGH")),
                                     method = c("default", "range"),
                                     na_str = "<Missing>") {
  method <- match.arg(method)
  checkmate::assert_subset(c("anl", "split_rows"), names(variables))
  checkmate::assert_false(anyNA(df[variables$split_rows]))
  assert_df_with_variables(df,
    variables = list(anl = variables$anl, split_rows = variables$split_rows),
    na_level = na_str
  )
  assert_df_with_factors(df, list(val = variables$anl))
  assert_valid_factor(df[[variables$anl]], any.missing = FALSE)
  assert_list_of_variables(variables)
  checkmate::assert_list(abnormal, types = "character", len = 2)

  # Drop usued levels from df as they are not supposed to be in the final map
  df <- droplevels(df)

  normal_value <- setdiff(levels(df[[variables$anl]]), unlist(abnormal))

  # Based on the understanding of clinical data, there should only be one level of normal which is "NORMAL"
  checkmate::assert_vector(normal_value, len = 1)

  # Default method will only have what is observed in the df, and records with all normal values will be excluded to
  # avoid error in layout building.
  if (method == "default") {
    df_abnormal <- subset(df, df[[variables$anl]] %in% unlist(abnormal))
    map <- unique(df_abnormal[c(variables$split_rows, variables$anl)])
    map_normal <- unique(subset(map, select = variables$split_rows))
    map_normal[[variables$anl]] <- normal_value
    map <- rbind(map, map_normal)
  } else if (method == "range") {
    # range method follows the rule that at least one observation with ANRLO > 0 for low
    # direction and at least one observation with ANRHI is not missing for high direction.
    checkmate::assert_subset(c("range_low", "range_high"), names(variables))
    checkmate::assert_subset(c("LOW", "HIGH"), toupper(names(abnormal)))

    assert_df_with_variables(df,
      variables = list(
        range_low = variables$range_low,
        range_high = variables$range_high
      )
    )

    # Define low direction of map
    df_low <- subset(df, df[[variables$range_low]] > 0)
    map_low <- unique(df_low[variables$split_rows])
    low_levels <- unname(unlist(abnormal[toupper(names(abnormal)) == "LOW"]))
    low_levels_df <- as.data.frame(low_levels)
    colnames(low_levels_df) <- variables$anl
    low_levels_df <- do.call("rbind", replicate(nrow(map_low), low_levels_df, simplify = FALSE))
    rownames(map_low) <- NULL # Just to avoid strange row index in case upstream functions changed
    map_low <- map_low[rep(seq_len(nrow(map_low)), each = length(low_levels)), , drop = FALSE]
    map_low <- cbind(map_low, low_levels_df)

    # Define high direction of map
    df_high <- subset(df, df[[variables$range_high]] != na_str | !is.na(df[[variables$range_high]]))
    map_high <- unique(df_high[variables$split_rows])
    high_levels <- unname(unlist(abnormal[toupper(names(abnormal)) == "HIGH"]))
    high_levels_df <- as.data.frame(high_levels)
    colnames(high_levels_df) <- variables$anl
    high_levels_df <- do.call("rbind", replicate(nrow(map_high), high_levels_df, simplify = FALSE))
    rownames(map_high) <- NULL
    map_high <- map_high[rep(seq_len(nrow(map_high)), each = length(high_levels)), , drop = FALSE]
    map_high <- cbind(map_high, high_levels_df)

    # Define normal of map
    map_normal <- unique(rbind(map_low, map_high)[variables$split_rows])
    map_normal[variables$anl] <- normal_value

    map <- rbind(map_low, map_high, map_normal)
  }

  # map should be all characters
  map <- data.frame(lapply(map, as.character), stringsAsFactors = FALSE)

  # sort the map final output by split_rows variables
  for (i in rev(seq_len(length(variables$split_rows)))) {
    map <- map[order(map[[i]]), ]
  }
  map
}

#' Encode Categorical Missing Values in a Data Frame
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a helper function to encode missing entries across groups of categorical
#' variables in a data frame.
#'
#' @details Missing entries are those with `NA` or empty strings and will
#'   be replaced with a specified value. If factor variables include missing
#'   values, the missing value will be inserted as the last level.
#'   Similarly, in case character or logical variables should be converted to factors
#'   with the `char_as_factor` or `logical_as_factor` options, the missing values will
#'   be set as the last level.
#'
#' @param data (`data.frame`)\cr data set.
#' @param omit_columns (`character`)\cr names of variables from `data` that should
#'   not be modified by this function.
#' @param char_as_factor (`flag`)\cr whether to convert character variables
#'   in `data` to factors.
#' @param logical_as_factor (`flag`)\cr whether to convert logical variables
#'   in `data` to factors.
#' @param na_level (`string`)\cr used to replace all `NA` or empty
#'   values inside non-`omit_columns` columns.
#'
#' @return A `data.frame` with the chosen modifications applied.
#'
#' @seealso [sas_na()] and [explicit_na()] for other missing data helper functions.
#'
#' @examples
#' my_data <- data.frame(
#'   u = c(TRUE, FALSE, NA, TRUE),
#'   v = factor(c("A", NA, NA, NA), levels = c("Z", "A")),
#'   w = c("A", "B", NA, "C"),
#'   x = c("D", "E", "F", NA),
#'   y = c("G", "H", "I", ""),
#'   z = c(1, 2, 3, 4),
#'   stringsAsFactors = FALSE
#' )
#'
#' # Example 1
#' # Encode missing values in all character or factor columns.
#' df_explicit_na(my_data)
#' # Also convert logical columns to factor columns.
#' df_explicit_na(my_data, logical_as_factor = TRUE)
#' # Encode missing values in a subset of columns.
#' df_explicit_na(my_data, omit_columns = c("x", "y"))
#'
#' # Example 2
#' # Here we purposefully convert all `M` values to `NA` in the `SEX` variable.
#' # After running `df_explicit_na` the `NA` values are encoded as `<Missing>` but they are not
#' # included when generating `rtables`.
#' adsl <- tern_ex_adsl
#' adsl$SEX[adsl$SEX == "M"] <- NA
#' adsl <- df_explicit_na(adsl)
#'
#' # If you want the `Na` values to be displayed in the table use the `na_level` argument.
#' adsl <- tern_ex_adsl
#' adsl$SEX[adsl$SEX == "M"] <- NA
#' adsl <- df_explicit_na(adsl, na_level = "Missing Values")
#'
#' # Example 3
#' # Numeric variables that have missing values are not altered. This means that any `NA` value in
#' # a numeric variable will not be included in the summary statistics, nor will they be included
#' # in the denominator value for calculating the percent values.
#' adsl <- tern_ex_adsl
#' adsl$AGE[adsl$AGE < 30] <- NA
#' adsl <- df_explicit_na(adsl)
#'
#' @export
df_explicit_na <- function(data,
                           omit_columns = NULL,
                           char_as_factor = TRUE,
                           logical_as_factor = FALSE,
                           na_level = "<Missing>") {
  checkmate::assert_character(omit_columns, null.ok = TRUE, min.len = 1, any.missing = FALSE)
  checkmate::assert_data_frame(data)
  checkmate::assert_flag(char_as_factor)
  checkmate::assert_flag(logical_as_factor)
  checkmate::assert_string(na_level)

  target_vars <- if (is.null(omit_columns)) {
    names(data)
  } else {
    setdiff(names(data), omit_columns) # May have duplicates.
  }
  if (length(target_vars) == 0) {
    return(data)
  }

  l_target_vars <- split(target_vars, target_vars)

  # Makes sure target_vars exist in data and names are not duplicated.
  assert_df_with_variables(data, l_target_vars)

  for (x in target_vars) {
    xi <- data[[x]]
    xi_label <- obj_label(xi)

    # Determine whether to convert character or logical input.
    do_char_conversion <- is.character(xi) && char_as_factor
    do_logical_conversion <- is.logical(xi) && logical_as_factor

    # Pre-convert logical to character to deal correctly with replacing NA
    # values below.
    if (do_logical_conversion) {
      xi <- as.character(xi)
    }

    if (is.factor(xi) || is.character(xi)) {
      # Handle empty strings and NA values.
      xi <- explicit_na(sas_na(xi), label = na_level)

      # Convert to factors if requested for the original type,
      # set na_level as the last value.
      if (do_char_conversion || do_logical_conversion) {
        levels_xi <- setdiff(sort(unique(xi)), na_level)
        if (na_level %in% unique(xi)) {
          levels_xi <- c(levels_xi, na_level)
        }

        xi <- factor(xi, levels = levels_xi)
      }

      data[, x] <- formatters::with_label(xi, label = xi_label)
    }
  }
  return(data)
}

#' Helper Functions for Cox Proportional Hazards Regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions used in [fit_coxreg_univar()] and [fit_coxreg_multivar()].
#'
#' @inheritParams argument_convention
#' @inheritParams h_coxreg_univar_extract
#' @inheritParams cox_regression_inter
#' @inheritParams control_coxreg
#'
#' @seealso [cox_regression]
#'
#' @name h_cox_regression
NULL

#' @describeIn h_cox_regression Helper for Cox regression formula. Creates a list of formulas. It is used
#'   internally by [fit_coxreg_univar()] for the comparison of univariate Cox regression models.
#'
#' @return
#' * `h_coxreg_univar_formulas()` returns a `character` vector coercible into formulas (e.g [stats::as.formula()]).
#'
#' @examples
#' # `h_coxreg_univar_formulas`
#'
#' ## Simple formulas.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", covariates = c("X", "y")
#'   )
#' )
#'
#' ## Addition of an optional strata.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
#'     strata = "SITE"
#'   )
#' )
#'
#' ## Inclusion of the interaction term.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
#'     strata = "SITE"
#'   ),
#'   interaction = TRUE
#' )
#'
#' ## Only covariates fitted in separate models.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", covariates = c("X", "y")
#'   )
#' )
#'
#' @export
h_coxreg_univar_formulas <- function(variables,
                                     interaction = FALSE) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  checkmate::assert_flag(interaction)

  if (!has_arm || is.null(variables$covariates)) {
    checkmate::assert_false(interaction)
  }

  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  if (!is.null(variables$covariates)) {
    forms <- paste0(
      "survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
      ifelse(has_arm, variables$arm, "1"),
      ifelse(interaction, " * ", " + "),
      variables$covariates,
      ifelse(
        !is.null(variables$strata),
        paste0(" + strata(", paste0(variables$strata, collapse = ", "), ")"),
        ""
      )
    )
  } else {
    forms <- NULL
  }
  nams <- variables$covariates
  if (has_arm) {
    ref <- paste0(
      "survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
      variables$arm,
      ifelse(
        !is.null(variables$strata),
        paste0(
          " + strata(", paste0(variables$strata, collapse = ", "), ")"
        ),
        ""
      )
    )
    forms <- c(ref, forms)
    nams <- c("ref", nams)
  }
  stats::setNames(forms, nams)
}

#' @describeIn h_cox_regression Helper for multivariate Cox regression formula. Creates a formulas
#'   string. It is used internally by [fit_coxreg_multivar()] for the comparison of multivariate Cox
#'   regression models. Interactions will not be included in multivariate Cox regression model.
#'
#' @return
#' * `h_coxreg_multivar_formula()` returns a `string` coercible into a formula (e.g [stats::as.formula()]).
#'
#' @examples
#' # `h_coxreg_multivar_formula`
#'
#' h_coxreg_multivar_formula(
#'   variables = list(
#'     time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE")
#'   )
#' )
#'
#' # Addition of an optional strata.
#' h_coxreg_multivar_formula(
#'   variables = list(
#'     time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE"),
#'     strata = "SITE"
#'   )
#' )
#'
#' # Example without treatment arm.
#' h_coxreg_multivar_formula(
#'   variables = list(
#'     time = "AVAL", event = "event", covariates = c("RACE", "AGE"),
#'     strata = "SITE"
#'   )
#' )
#'
#' @export
h_coxreg_multivar_formula <- function(variables) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  y <- paste0(
    "survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
    ifelse(has_arm, variables$arm, "1")
  )
  if (length(variables$covariates) > 0) {
    y <- paste(y, paste(variables$covariates, collapse = " + "), sep = " + ")
  }
  if (!is.null(variables$strata)) {
    y <- paste0(y, " + strata(", paste0(variables$strata, collapse = ", "), ")")
  }
  y
}

#' @describeIn h_cox_regression Utility function to help tabulate the result of
#'   a univariate Cox regression model.
#'
#' @param effect (`string`)\cr the treatment variable.
#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
#'
#' @return
#' * `h_coxreg_univar_extract()` returns a `data.frame` with variables `effect`, `term`, `term_label`, `level`,
#'   `n`, `hr`, `lcl`, `ucl`, and `pval`.
#'
#' @examples
#' library(survival)
#'
#' dta_simple <- data.frame(
#'   time = c(5, 5, 10, 10, 5, 5, 10, 10),
#'   status = c(0, 0, 1, 0, 0, 1, 1, 1),
#'   armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c("A", "B")),
#'   var1 = c(45, 55, 65, 75, 55, 65, 85, 75),
#'   var2 = c("F", "M", "F", "M", "F", "M", "F", "U")
#' )
#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
#' result <- h_coxreg_univar_extract(
#'   effect = "armcd", covar = "armcd", mod = mod, data = dta_simple
#' )
#' result
#'
#' @export
h_coxreg_univar_extract <- function(effect,
                                    covar,
                                    data,
                                    mod,
                                    control = control_coxreg()) {
  checkmate::assert_string(covar)
  checkmate::assert_string(effect)
  checkmate::assert_class(mod, "coxph")
  test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]

  mod_aov <- muffled_car_anova(mod, test_statistic)
  msum <- summary(mod, conf.int = control$conf_level)
  sum_cox <- broom::tidy(msum)

  # Combine results together.
  effect_aov <- mod_aov[effect, , drop = TRUE]
  pval <- effect_aov[[grep(pattern = "Pr", x = names(effect_aov)), drop = TRUE]]
  sum_main <- sum_cox[grepl(effect, sum_cox$level), ]

  term_label <- if (effect == covar) {
    paste0(
      levels(data[[covar]])[2],
      " vs control (",
      levels(data[[covar]])[1],
      ")"
    )
  } else {
    unname(labels_or_names(data[covar]))
  }
  data.frame(
    effect = ifelse(covar == effect, "Treatment:", "Covariate:"),
    term = covar,
    term_label = term_label,
    level = levels(data[[effect]])[2],
    n = mod[["n"]],
    hr = unname(sum_main["exp(coef)"]),
    lcl = unname(sum_main[grep("lower", names(sum_main))]),
    ucl = unname(sum_main[grep("upper", names(sum_main))]),
    pval = pval,
    stringsAsFactors = FALSE
  )
}

#' @describeIn h_cox_regression Tabulation of multivariate Cox regressions. Utility function to help
#'   tabulate the result of a multivariate Cox regression model for a treatment/covariate variable.
#'
#' @return
#' * `h_coxreg_multivar_extract()` returns a `data.frame` with variables `pval`, `hr`, `lcl`, `ucl`, `level`,
#'   `n`, `term`, and `term_label`.
#'
#' @examples
#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
#' result <- h_coxreg_multivar_extract(
#'   var = "var1", mod = mod, data = dta_simple
#' )
#' result
#'
#' @export
h_coxreg_multivar_extract <- function(var,
                                      data,
                                      mod,
                                      control = control_coxreg()) {
  test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
  mod_aov <- muffled_car_anova(mod, test_statistic)

  msum <- summary(mod, conf.int = control$conf_level)
  sum_anova <- broom::tidy(mod_aov)
  sum_cox <- broom::tidy(msum)

  ret_anova <- sum_anova[sum_anova$term == var, c("term", "p.value")]
  names(ret_anova)[2] <- "pval"
  if (is.factor(data[[var]])) {
    ret_cox <- sum_cox[startsWith(prefix = var, x = sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
  } else {
    ret_cox <- sum_cox[(var == sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
  }
  names(ret_cox)[1:4] <- c("pval", "hr", "lcl", "ucl")
  varlab <- unname(labels_or_names(data[var]))
  ret_cox$term <- varlab

  if (is.numeric(data[[var]])) {
    ret <- ret_cox
    ret$term_label <- ret$term
  } else if (length(levels(data[[var]])) <= 2) {
    ret_anova$pval <- NA
    ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
    ret_cox$level <- gsub(var, "", ret_cox$level)
    ret_cox$term_label <- ret_cox$level
    ret <- dplyr::bind_rows(ret_anova, ret_cox)
  } else {
    ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
    ret_cox$level <- gsub(var, "", ret_cox$level)
    ret_cox$term_label <- ret_cox$level
    ret <- dplyr::bind_rows(ret_anova, ret_cox)
  }

  as.data.frame(ret)
}

#' Summary for Poisson Negative Binomial.
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Summarize results of a Poisson Negative Binomial Regression.
#' This can be used to analyze count and/or frequency data using a linear model.
#'
#' @inheritParams h_glm_count
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("summarize_glm_count")`
#'   to see available statistics for this function.
#'
#' @name summarize_glm_count
#' @order 1
NULL

#' Helper Functions for Poisson Models.
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Helper functions that can be used to return the results of various Poisson models.
#'
#' @inheritParams argument_convention
#'
#' @seealso [summarize_glm_count]
#'
#' @name h_glm_count
NULL

#' @describeIn h_glm_count Helper function to return results of a poisson model.
#'
#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called
#'   in `.var` and `variables`.
#' @param variables (named `list` of `strings`)\cr list of additional analysis variables, with
#'   expected elements:
#'   * `arm` (`string`)\cr group variable, for which the covariate adjusted means of multiple
#'     groups will be summarized. Specifically, the first level of `arm` variable is taken as the
#'     reference group.
#'   * `covariates` (`character`)\cr a vector that can contain single variable names (such as
#'     `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
#'   * `offset` (`numeric`)\cr a numeric vector or scalar adding an offset.
#' @param weights (`character`)\cr a character vector specifying weights used
#'   in averaging predictions. Number of weights must equal the number of levels included in the covariates.
#'   Weights option passed to [emmeans::emmeans()].
#'
#' @return
#' * `h_glm_poisson()` returns the results of a Poisson model.
#'
#' @keywords internal
h_glm_poisson <- function(.var,
                          .df_row,
                          variables,
                          weights) {
  arm <- variables$arm
  covariates <- variables$covariates
  offset <- .df_row[[variables$offset]]

  formula <- stats::as.formula(paste0(
    .var, " ~ ",
    " + ",
    paste(covariates, collapse = " + "),
    " + ",
    arm
  ))

  glm_fit <- stats::glm(
    formula = formula,
    offset = offset,
    data = .df_row,
    family = stats::poisson(link = "log")
  )

  emmeans_fit <- emmeans::emmeans(
    glm_fit,
    specs = arm,
    data = .df_row,
    type = "response",
    offset = 0,
    weights = weights
  )

  list(
    glm_fit = glm_fit,
    emmeans_fit = emmeans_fit
  )
}

#' @describeIn h_glm_count Helper function to return results of a quasipoisson model.
#'
#' @inheritParams summarize_glm_count
#'
#' @return
#' * `h_glm_quasipoisson()` returns the results of a Quasi-Poisson model.
#'
#' @keywords internal
h_glm_quasipoisson <- function(.var,
                               .df_row,
                               variables,
                               weights) {
  arm <- variables$arm
  covariates <- variables$covariates
  offset <- .df_row[[variables$offset]]

  formula <- stats::as.formula(paste0(
    .var, " ~ ",
    " + ",
    paste(covariates, collapse = " + "),
    " + ",
    arm
  ))

  glm_fit <- stats::glm(
    formula = formula,
    offset = offset,
    data = .df_row,
    family = stats::quasipoisson(link = "log")
  )

  emmeans_fit <- emmeans::emmeans(
    glm_fit,
    specs = arm,
    data = .df_row,
    type = "response",
    offset = 0,
    weights = weights
  )

  list(
    glm_fit = glm_fit,
    emmeans_fit = emmeans_fit
  )
}

#' @describeIn h_glm_count Helper function to return results of a negative binomial model.
#'
#' @inheritParams summarize_glm_count
#'
#' @return
#' * `h_glm_negbin()` returns the results of a Negative Binomial model.
#'
#' @keywords internal
h_glm_negbin <- function(.var,
                         .df_row,
                         variables,
                         weights) {
  arm <- variables$arm
  covariates <- variables$covariates

  formula <- stats::as.formula(paste0(
    .var, " ~ ",
    " + ",
    paste(covariates, collapse = " + "),
    " + ",
    arm
  ))

  glm_fit <- MASS::glm.nb(
    formula = formula,
    data = .df_row,
    link = "log"
  )

  emmeans_fit <- emmeans::emmeans(
    glm_fit,
    specs = arm,
    data = .df_row,
    type = "response",
    offset = 0,
    weights = weights
  )

  list(
    glm_fit = glm_fit,
    emmeans_fit = emmeans_fit
  )
}

#' @describeIn h_glm_count Helper function to return the results of the
#'   selected model (poisson, quasipoisson, negative binomial).
#'
#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called
#'   in `.var` and `variables`.
#' @param variables (named `list` of `strings`)\cr list of additional analysis variables, with
#'   expected elements:
#'   * `arm` (`string`)\cr group variable, for which the covariate adjusted means of multiple
#'     groups will be summarized. Specifically, the first level of `arm` variable is taken as the
#'     reference group.
#'   * `covariates` (`character`)\cr a vector that can contain single variable names (such as
#'     `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
#'   * `offset` (`numeric`)\cr a numeric vector or scalar adding an offset.
#' @param distribution (`character`)\cr a character value specifying the distribution
#'   used in the regression (poisson, quasipoisson, negative binomial).
#'
#' @return
#' * `h_glm_count()` returns the results of the selected model.
#'
#' @keywords internal
h_glm_count <- function(.var,
                        .df_row,
                        variables,
                        distribution,
                        weights) {
  checkmate::assert_subset(distribution, c("poisson", "quasipoisson", "negbin"), empty.ok = FALSE)
  switch(distribution,
    poisson = h_glm_poisson(.var, .df_row, variables, weights),
    quasipoisson = h_glm_quasipoisson(.var, .df_row, variables, weights),
    negbin = h_glm_negbin(.var, .df_row, variables, weights)
  )
}

#' @describeIn h_glm_count Helper function to return the estimated means.
#'
#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called in `.var` and `variables`.
#' @param conf_level (`numeric`)\cr value used to derive the confidence interval for the rate.
#' @param obj (`glm.fit`)\cr fitted model object used to derive the mean rate estimates in each treatment arm.
#' @param arm (`string`)\cr group variable, for which the covariate adjusted means of multiple groups will be
#'   summarized. Specifically, the first level of `arm` variable is taken as the reference group.
#'
#' @return
#' * `h_ppmeans()` returns the estimated means.
#'
#' @keywords internal
h_ppmeans <- function(obj, .df_row, arm, conf_level) {
  alpha <- 1 - conf_level
  p <- 1 - alpha / 2

  arm_levels <- levels(.df_row[[arm]])

  out <- lapply(arm_levels, function(lev) {
    temp <- .df_row
    temp[[arm]] <- factor(lev, levels = arm_levels)

    mf <- stats::model.frame(obj$formula, data = temp)
    X <- stats::model.matrix(obj$formula, data = mf) # nolint

    rate <- stats::predict(obj, newdata = mf, type = "response")
    rate_hat <- mean(rate)

    zz <- colMeans(rate * X)
    se <- sqrt(as.numeric(t(zz) %*% stats::vcov(obj) %*% zz))
    rate_lwr <- rate_hat * exp(-stats::qnorm(p) * se / rate_hat)
    rate_upr <- rate_hat * exp(stats::qnorm(p) * se / rate_hat)

    c(rate_hat, rate_lwr, rate_upr)
  })

  names(out) <- arm_levels
  out <- do.call(rbind, out)
  if ("negbin" %in% class(obj)) {
    colnames(out) <- c("response", "asymp.LCL", "asymp.UCL")
  } else {
    colnames(out) <- c("rate", "asymp.LCL", "asymp.UCL")
  }
  out <- as.data.frame(out)
  out[[arm]] <- rownames(out)
  out
}

#' @describeIn summarize_glm_count Statistics function that produces a named list of results
#'   of the investigated Poisson model.
#'
#' @return
#' * `s_glm_count()` returns a named `list` of 5 statistics:
#'   * `n`: Count of complete sample size for the group.
#'   * `rate`: Estimated event rate per follow-up time.
#'   * `rate_ci`: Confidence level for estimated rate per follow-up time.
#'   * `rate_ratio`: Ratio of event rates in each treatment arm to the reference arm.
#'   * `rate_ratio_ci`: Confidence level for the rate ratio.
#'   * `pval`: p-value.
#'
#' @keywords internal
s_glm_count <- function(df,
                        .var,
                        .df_row,
                        variables,
                        .ref_group,
                        .in_ref_col,
                        distribution,
                        conf_level,
                        rate_mean_method,
                        weights,
                        scale = 1) {
  arm <- variables$arm

  y <- df[[.var]]
  smry_level <- as.character(unique(df[[arm]]))

  # ensure there is only 1 value
  checkmate::assert_scalar(smry_level)

  results <- h_glm_count(
    .var = .var,
    .df_row = .df_row,
    variables = variables,
    distribution = distribution,
    weights
  )

  if (rate_mean_method == "emmeans") {
    emmeans_smry <- summary(results$emmeans_fit, level = conf_level)
  } else if (rate_mean_method == "ppmeans") {
    emmeans_smry <- h_ppmeans(results$glm_fit, .df_row, arm, conf_level)
  }

  emmeans_smry_level <- emmeans_smry[emmeans_smry[[arm]] == smry_level, ]

  if (.in_ref_col) {
    list(
      n = length(y[!is.na(y)]),
      rate = formatters::with_label(
        ifelse(distribution == "negbin", emmeans_smry_level$response * scale, emmeans_smry_level$rate),
        "Adjusted Rate"
      ),
      rate_ci = formatters::with_label(
        c(emmeans_smry_level$asymp.LCL * scale, emmeans_smry_level$asymp.UCL * scale),
        f_conf_level(conf_level)
      ),
      rate_ratio = formatters::with_label(character(), "Adjusted Rate Ratio"),
      rate_ratio_ci = formatters::with_label(character(), f_conf_level(conf_level)),
      pval = formatters::with_label(character(), "p-value")
    )
  } else {
    emmeans_contrasts <- emmeans::contrast(
      results$emmeans_fit,
      method = "trt.vs.ctrl",
      ref = grep(
        as.character(unique(.ref_group[[arm]])),
        as.data.frame(results$emmeans_fit)[[arm]]
      )
    )

    contrasts_smry <- summary(
      emmeans_contrasts,
      infer = TRUE,
      adjust = "none"
    )

    smry_contrasts_level <- contrasts_smry[grepl(smry_level, contrasts_smry$contrast), ]

    list(
      n = length(y[!is.na(y)]),
      rate = formatters::with_label(
        ifelse(distribution == "negbin", emmeans_smry_level$response * scale, emmeans_smry_level$rate),
        "Adjusted Rate"
      ),
      rate_ci = formatters::with_label(
        c(emmeans_smry_level$asymp.LCL * scale, emmeans_smry_level$asymp.UCL * scale),
        f_conf_level(conf_level)
      ),
      rate_ratio = formatters::with_label(smry_contrasts_level$ratio, "Adjusted Rate Ratio"),
      rate_ratio_ci = formatters::with_label(
        c(smry_contrasts_level$asymp.LCL, smry_contrasts_level$asymp.UCL),
        f_conf_level(conf_level)
      ),
      pval = formatters::with_label(smry_contrasts_level$p.value, "p-value")
    )
  }
}

#' @describeIn summarize_glm_count Formatted analysis function which is used as `afun` in `summarize_glm_count()`.
#'
#' @return
#' * `a_glm_count()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_glm_count <- make_afun(
  s_glm_count,
  .indent_mods = c(
    "n" = 0L,
    "rate" = 0L,
    "rate_ci" = 1L,
    "rate_ratio" = 0L,
    "rate_ratio_ci" = 1L,
    "pval" = 1L
  ),
  .formats = c(
    "n" = "xx",
    "rate" = "xx.xxxx",
    "rate_ci" = "(xx.xxxx, xx.xxxx)",
    "rate_ratio" = "xx.xxxx",
    "rate_ratio_ci" = "(xx.xxxx, xx.xxxx)",
    "pval" = "x.xxxx | (<0.0001)"
  ),
  .null_ref_cells = FALSE
)

#' @describeIn summarize_glm_count Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `summarize_glm_count()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_glm_count()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' anl <- tern_ex_adtte %>% filter(PARAMCD == "TNE")
#' anl$AVAL_f <- as.factor(anl$AVAL)
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM", ref_group = "B: Placebo") %>%
#'   add_colcounts() %>%
#'   analyze_vars(
#'     "AVAL_f",
#'     var_labels = "Number of exacerbations per patient",
#'     .stats = c("count_fraction"),
#'     .formats = c("count_fraction" = "xx (xx.xx%)"),
#'     .labels = c("Number of exacerbations per patient")
#'   ) %>%
#'   summarize_glm_count(
#'     vars = "AVAL",
#'     variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = NULL),
#'     conf_level = 0.95,
#'     distribution = "poisson",
#'     rate_mean_method = "emmeans",
#'     var_labels = "Unadjusted exacerbation rate (per year)",
#'     table_names = "unadj",
#'     .stats = c("rate"),
#'     .labels = c(rate = "Rate")
#'   ) %>%
#'   summarize_glm_count(
#'     vars = "AVAL",
#'     variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
#'     conf_level = 0.95,
#'     distribution = "quasipoisson",
#'     rate_mean_method = "ppmeans",
#'     var_labels = "Adjusted (QP) exacerbation rate (per year)",
#'     table_names = "adj",
#'     .stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
#'     .labels = c(
#'       rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
#'       rate_ratio_ci = "Rate Ratio CI", pval = "p value"
#'     )
#'   )
#'
#' build_table(lyt = lyt, df = anl)
#'
#' @export
#' @order 2
summarize_glm_count <- function(lyt,
                                vars,
                                variables,
                                distribution,
                                conf_level,
                                rate_mean_method,
                                weights = stats::weights,
                                scale = 1,
                                var_labels,
                                na_str = default_na_str(),
                                nested = TRUE,
                                ...,
                                show_labels = "visible",
                                table_names = vars,
                                .stats = NULL,
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = NULL) {
  extra_args <- list(
    variables = variables, distribution = distribution, conf_level = conf_level,
    rate_mean_method = rate_mean_method, weights = weights, scale = scale, ...
  )

  afun <- make_afun(
    a_glm_count,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Estimation of Proportions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Estimate the proportion of responders within a studied population.
#'
#' @inheritParams prop_strat_wilson
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_proportion")`
#'   to see available statistics for this function.
#' @param method (`string`)\cr the method used to construct the confidence interval
#'   for proportion of successful outcomes; one of `waldcc`, `wald`, `clopper-pearson`,
#'   `wilson`, `wilsonc`, `strat_wilson`, `strat_wilsonc`, `agresti-coull` or `jeffreys`.
#' @param long (`flag`)\cr a long description is required.
#'
#' @seealso [h_proportions]
#'
#' @name estimate_proportions
#' @order 1
NULL

#' @describeIn estimate_proportions Statistics function estimating a
#'   proportion along with its confidence interval.
#'
#' @param df (`logical` or `data.frame`)\cr if only a logical vector is used,
#'   it indicates whether each subject is a responder or not. `TRUE` represents
#'   a successful outcome. If a `data.frame` is provided, also the `strata` variable
#'   names must be provided in `variables` as a list element with the strata strings.
#'   In the case of `data.frame`, the logical vector of responses must be indicated as a
#'   variable name in `.var`.
#'
#' @return
#' * `s_proportion()` returns statistics `n_prop` (`n` and proportion) and `prop_ci` (proportion CI) for a
#'   given variable.
#'
#' @examples
#' # Case with only logical vector.
#' rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0)
#' s_proportion(rsp_v)
#'
#' # Example for Stratified Wilson CI
#' nex <- 100 # Number of example rows
#' dta <- data.frame(
#'   "rsp" = sample(c(TRUE, FALSE), nex, TRUE),
#'   "grp" = sample(c("A", "B"), nex, TRUE),
#'   "f1" = sample(c("a1", "a2"), nex, TRUE),
#'   "f2" = sample(c("x", "y", "z"), nex, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' s_proportion(
#'   df = dta,
#'   .var = "rsp",
#'   variables = list(strata = c("f1", "f2")),
#'   conf_level = 0.90,
#'   method = "strat_wilson"
#' )
#'
#' @export
s_proportion <- function(df,
                         .var,
                         conf_level = 0.95,
                         method = c(
                           "waldcc", "wald", "clopper-pearson",
                           "wilson", "wilsonc", "strat_wilson", "strat_wilsonc",
                           "agresti-coull", "jeffreys"
                         ),
                         weights = NULL,
                         max_iterations = 50,
                         variables = list(strata = NULL),
                         long = FALSE) {
  method <- match.arg(method)
  checkmate::assert_flag(long)
  assert_proportion_value(conf_level)

  if (!is.null(variables$strata)) {
    # Checks for strata
    if (missing(df)) stop("When doing stratified analysis a data.frame with specific columns is needed.")
    strata_colnames <- variables$strata
    checkmate::assert_character(strata_colnames, null.ok = FALSE)
    strata_vars <- stats::setNames(as.list(strata_colnames), strata_colnames)
    assert_df_with_variables(df, strata_vars)

    strata <- interaction(df[strata_colnames])
    strata <- as.factor(strata)

    # Pushing down checks to prop_strat_wilson
  } else if (checkmate::test_subset(method, c("strat_wilson", "strat_wilsonc"))) {
    stop("To use stratified methods you need to specify the strata variables.")
  }
  if (checkmate::test_atomic_vector(df)) {
    rsp <- as.logical(df)
  } else {
    rsp <- as.logical(df[[.var]])
  }
  n <- sum(rsp)
  p_hat <- mean(rsp)

  prop_ci <- switch(method,
    "clopper-pearson" = prop_clopper_pearson(rsp, conf_level),
    "wilson" = prop_wilson(rsp, conf_level),
    "wilsonc" = prop_wilson(rsp, conf_level, correct = TRUE),
    "strat_wilson" = prop_strat_wilson(rsp,
      strata,
      weights,
      conf_level,
      max_iterations,
      correct = FALSE
    )$conf_int,
    "strat_wilsonc" = prop_strat_wilson(rsp,
      strata,
      weights,
      conf_level,
      max_iterations,
      correct = TRUE
    )$conf_int,
    "wald" = prop_wald(rsp, conf_level),
    "waldcc" = prop_wald(rsp, conf_level, correct = TRUE),
    "agresti-coull" = prop_agresti_coull(rsp, conf_level),
    "jeffreys" = prop_jeffreys(rsp, conf_level)
  )

  list(
    "n_prop" = formatters::with_label(c(n, p_hat), "Responders"),
    "prop_ci" = formatters::with_label(
      x = 100 * prop_ci, label = d_proportion(conf_level, method, long = long)
    )
  )
}

#' @describeIn estimate_proportions Formatted analysis function which is used as `afun`
#'   in `estimate_proportion()`.
#'
#' @return
#' * `a_proportion()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @export
a_proportion <- make_afun(
  s_proportion,
  .formats = c(n_prop = "xx (xx.x%)", prop_ci = "(xx.x, xx.x)")
)

#' @describeIn estimate_proportions Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `estimate_proportion()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_proportion()` to the table layout.
#'
#' @examples
#' dta_test <- data.frame(
#'   USUBJID = paste0("S", 1:12),
#'   ARM     = rep(LETTERS[1:3], each = 4),
#'   AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0))
#' )
#'
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   estimate_proportion(vars = "AVAL") %>%
#'   build_table(df = dta_test)
#'
#' @export
#' @order 2
estimate_proportion <- function(lyt,
                                vars,
                                conf_level = 0.95,
                                method = c(
                                  "waldcc", "wald", "clopper-pearson",
                                  "wilson", "wilsonc", "strat_wilson", "strat_wilsonc",
                                  "agresti-coull", "jeffreys"
                                ),
                                weights = NULL,
                                max_iterations = 50,
                                variables = list(strata = NULL),
                                long = FALSE,
                                na_str = default_na_str(),
                                nested = TRUE,
                                ...,
                                show_labels = "hidden",
                                table_names = vars,
                                .stats = NULL,
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = NULL) {
  extra_args <- list(
    conf_level = conf_level, method = method, weights = weights, max_iterations = max_iterations,
    variables = variables, long = long, ...
  )

  afun <- make_afun(
    a_proportion,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  analyze(
    lyt,
    vars,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Helper Functions for Calculating Proportion Confidence Intervals
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions to calculate different proportion confidence intervals for use in [estimate_proportion()].
#'
#' @inheritParams argument_convention
#' @inheritParams estimate_proportions
#'
#' @return Confidence interval of a proportion.
#'
#' @seealso [estimate_proportions], descriptive function [d_proportion()],
#'  and helper functions [strata_normal_quantile()] and [update_weights_strat_wilson()].
#'
#' @name h_proportions
NULL

#' @describeIn h_proportions Calculates the Wilson interval by calling [stats::prop.test()].
#'  Also referred to as Wilson score interval.
#'
#' @examples
#' rsp <- c(
#'   TRUE, TRUE, TRUE, TRUE, TRUE,
#'   FALSE, FALSE, FALSE, FALSE, FALSE
#' )
#' prop_wilson(rsp, conf_level = 0.9)
#'
#' @export
prop_wilson <- function(rsp, conf_level, correct = FALSE) {
  y <- stats::prop.test(
    sum(rsp),
    length(rsp),
    correct = correct,
    conf.level = conf_level
  )

  as.numeric(y$conf.int)
}

#' @describeIn h_proportions Calculates the stratified Wilson confidence
#'   interval for unequal proportions as described in \insertCite{Yan2010-jt;textual}{tern}
#'
#' @param strata (`factor`)\cr variable with one level per stratum and same length as `rsp`.
#' @param weights (`numeric` or `NULL`)\cr weights for each level of the strata. If `NULL`, they are
#'   estimated using the iterative algorithm proposed in \insertCite{Yan2010-jt;textual}{tern} that
#'   minimizes the weighted squared length of the confidence interval.
#' @param max_iterations (`count`)\cr maximum number of iterations for the iterative procedure used
#'   to find estimates of optimal weights.
#' @param correct (`flag`)\cr include the continuity correction. For further information, see for example
#'   [stats::prop.test()].
#'
#' @references
#' \insertRef{Yan2010-jt}{tern}
#'
#' @examples
#' # Stratified Wilson confidence interval with unequal probabilities
#'
#' set.seed(1)
#' rsp <- sample(c(TRUE, FALSE), 100, TRUE)
#' strata_data <- data.frame(
#'   "f1" = sample(c("a", "b"), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' )
#' strata <- interaction(strata_data)
#' n_strata <- ncol(table(rsp, strata)) # Number of strata
#'
#' prop_strat_wilson(
#'   rsp = rsp, strata = strata,
#'   conf_level = 0.90
#' )
#'
#' # Not automatic setting of weights
#' prop_strat_wilson(
#'   rsp = rsp, strata = strata,
#'   weights = rep(1 / n_strata, n_strata),
#'   conf_level = 0.90
#' )
#'
#' @export
prop_strat_wilson <- function(rsp,
                              strata,
                              weights = NULL,
                              conf_level = 0.95,
                              max_iterations = NULL,
                              correct = FALSE) {
  checkmate::assert_logical(rsp, any.missing = FALSE)
  checkmate::assert_factor(strata, len = length(rsp))
  assert_proportion_value(conf_level)

  tbl <- table(rsp, strata)
  n_strata <- length(unique(strata))

  # Checking the weights and maximum number of iterations.
  do_iter <- FALSE
  if (is.null(weights)) {
    weights <- rep(1 / n_strata, n_strata) # Initialization for iterative procedure
    do_iter <- TRUE

    # Iteration parameters
    if (is.null(max_iterations)) max_iterations <- 10
    checkmate::assert_int(max_iterations, na.ok = FALSE, null.ok = FALSE, lower = 1)
  }
  checkmate::assert_numeric(weights, lower = 0, upper = 1, any.missing = FALSE, len = n_strata)
  sum_weights <- checkmate::assert_int(sum(weights))
  if (as.integer(sum_weights + 0.5) != 1L) stop("Sum of weights must be 1L.")


  xs <- tbl["TRUE", ]
  ns <- colSums(tbl)
  use_stratum <- (ns > 0)
  ns <- ns[use_stratum]
  xs <- xs[use_stratum]
  ests <- xs / ns
  vars <- ests * (1 - ests) / ns

  strata_qnorm <- strata_normal_quantile(vars, weights, conf_level)

  # Iterative setting of weights if they were not set externally
  weights_new <- if (do_iter) {
    update_weights_strat_wilson(vars, strata_qnorm, weights, ns, max_iterations, conf_level)$weights
  } else {
    weights
  }

  strata_conf_level <- 2 * stats::pnorm(strata_qnorm) - 1

  ci_by_strata <- Map(
    function(x, n) {
      # Classic Wilson's confidence interval
      suppressWarnings(stats::prop.test(x, n, correct = correct, conf.level = strata_conf_level)$conf.int)
    },
    x = xs,
    n = ns
  )
  lower_by_strata <- sapply(ci_by_strata, "[", 1L)
  upper_by_strata <- sapply(ci_by_strata, "[", 2L)

  lower <- sum(weights_new * lower_by_strata)
  upper <- sum(weights_new * upper_by_strata)

  # Return values
  if (do_iter) {
    list(
      conf_int = c(
        lower = lower,
        upper = upper
      ),
      weights = weights_new
    )
  } else {
    list(
      conf_int = c(
        lower = lower,
        upper = upper
      )
    )
  }
}

#' @describeIn h_proportions Calculates the Clopper-Pearson interval by calling [stats::binom.test()].
#'   Also referred to as the `exact` method.
#'
#' @examples
#' prop_clopper_pearson(rsp, conf_level = .95)
#'
#' @export
prop_clopper_pearson <- function(rsp,
                                 conf_level) {
  y <- stats::binom.test(
    x = sum(rsp),
    n = length(rsp),
    conf.level = conf_level
  )
  as.numeric(y$conf.int)
}

#' @describeIn h_proportions Calculates the Wald interval by following the usual textbook definition
#'   for a single proportion confidence interval using the normal approximation.
#'
#' @param correct (`flag`)\cr apply continuity correction.
#'
#' @examples
#' prop_wald(rsp, conf_level = 0.95)
#' prop_wald(rsp, conf_level = 0.95, correct = TRUE)
#'
#' @export
prop_wald <- function(rsp, conf_level, correct = FALSE) {
  n <- length(rsp)
  p_hat <- mean(rsp)
  z <- stats::qnorm((1 + conf_level) / 2)
  q_hat <- 1 - p_hat
  correct <- if (correct) 1 / (2 * n) else 0

  err <- z * sqrt(p_hat * q_hat) / sqrt(n) + correct
  l_ci <- max(0, p_hat - err)
  u_ci <- min(1, p_hat + err)

  c(l_ci, u_ci)
}

#' @describeIn h_proportions Calculates the `Agresti-Coull` interval (created by `Alan Agresti` and `Brent Coull`) by
#'   (for 95% CI) adding two successes and two failures to the data and then using the Wald formula to construct a CI.
#'
#' @examples
#' prop_agresti_coull(rsp, conf_level = 0.95)
#'
#' @export
prop_agresti_coull <- function(rsp, conf_level) {
  n <- length(rsp)
  x_sum <- sum(rsp)
  z <- stats::qnorm((1 + conf_level) / 2)

  # Add here both z^2 / 2 successes and failures.
  x_sum_tilde <- x_sum + z^2 / 2
  n_tilde <- n + z^2

  # Then proceed as with the Wald interval.
  p_tilde <- x_sum_tilde / n_tilde
  q_tilde <- 1 - p_tilde
  err <- z * sqrt(p_tilde * q_tilde) / sqrt(n_tilde)
  l_ci <- max(0, p_tilde - err)
  u_ci <- min(1, p_tilde + err)

  c(l_ci, u_ci)
}

#' @describeIn h_proportions Calculates the Jeffreys interval, an equal-tailed interval based on the
#'   non-informative Jeffreys prior for a binomial proportion.
#'
#' @examples
#' prop_jeffreys(rsp, conf_level = 0.95)
#'
#' @export
prop_jeffreys <- function(rsp,
                          conf_level) {
  n <- length(rsp)
  x_sum <- sum(rsp)

  alpha <- 1 - conf_level
  l_ci <- ifelse(
    x_sum == 0,
    0,
    stats::qbeta(alpha / 2, x_sum + 0.5, n - x_sum + 0.5)
  )

  u_ci <- ifelse(
    x_sum == n,
    1,
    stats::qbeta(1 - alpha / 2, x_sum + 0.5, n - x_sum + 0.5)
  )

  c(l_ci, u_ci)
}

#' Description of the Proportion Summary
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a helper function that describes the analysis in [s_proportion()].
#'
#' @inheritParams s_proportion
#' @param long (`flag`)\cr whether a long or a short (default) description is required.
#'
#' @return String describing the analysis.
#'
#' @export
d_proportion <- function(conf_level,
                         method,
                         long = FALSE) {
  label <- paste0(conf_level * 100, "% CI")

  if (long) label <- paste(label, "for Response Rates")

  method_part <- switch(method,
    "clopper-pearson" = "Clopper-Pearson",
    "waldcc" = "Wald, with correction",
    "wald" = "Wald, without correction",
    "wilson" = "Wilson, without correction",
    "strat_wilson" = "Stratified Wilson, without correction",
    "wilsonc" = "Wilson, with correction",
    "strat_wilsonc" = "Stratified Wilson, with correction",
    "agresti-coull" = "Agresti-Coull",
    "jeffreys" = "Jeffreys",
    stop(paste(method, "does not have a description"))
  )

  paste0(label, " (", method_part, ")")
}

#' Helper Function for the Estimation of Stratified Quantiles
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This function wraps the estimation of stratified percentiles when we assume
#' the approximation for large numbers. This is necessary only in the case
#' proportions for each strata are unequal.
#'
#' @inheritParams argument_convention
#' @inheritParams prop_strat_wilson
#'
#' @return Stratified quantile.
#'
#' @seealso [prop_strat_wilson()]
#'
#' @examples
#' strata_data <- table(data.frame(
#'   "f1" = sample(c(TRUE, FALSE), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' ))
#' ns <- colSums(strata_data)
#' ests <- strata_data["TRUE", ] / ns
#' vars <- ests * (1 - ests) / ns
#' weights <- rep(1 / length(ns), length(ns))
#'
#' strata_normal_quantile(vars, weights, 0.95)
#'
#' @export
strata_normal_quantile <- function(vars, weights, conf_level) {
  summands <- weights^2 * vars
  # Stratified quantile
  sqrt(sum(summands)) / sum(sqrt(summands)) * stats::qnorm((1 + conf_level) / 2)
}

#' Helper Function for the Estimation of Weights for `prop_strat_wilson`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This function wraps the iteration procedure that allows you to estimate
#' the weights for each proportional strata. This assumes to minimize the
#' weighted squared length of the confidence interval.
#'
#' @inheritParams prop_strat_wilson
#' @param vars (`numeric`)\cr normalized proportions for each strata.
#' @param strata_qnorm (`numeric`)\cr initial estimation with identical weights of the quantiles.
#' @param initial_weights (`numeric`)\cr initial weights used to calculate `strata_qnorm`. This can
#'   be optimized in the future if we need to estimate better initial weights.
#' @param n_per_strata (`numeric`)\cr number of elements in each strata.
#' @param max_iterations (`count`)\cr maximum number of iterations to be tried. Convergence is always checked.
#' @param tol (`number`)\cr tolerance threshold for convergence.
#'
#' @return A `list` of 3 elements: `n_it`, `weights`, and `diff_v`.
#'
#' @seealso For references and details see [prop_strat_wilson()].
#'
#' @examples
#' vs <- c(0.011, 0.013, 0.012, 0.014, 0.017, 0.018)
#' sq <- 0.674
#' ws <- rep(1 / length(vs), length(vs))
#' ns <- c(22, 18, 17, 17, 14, 12)
#'
#' update_weights_strat_wilson(vs, sq, ws, ns, 100, 0.95, 0.001)
#'
#' @export
update_weights_strat_wilson <- function(vars,
                                        strata_qnorm,
                                        initial_weights,
                                        n_per_strata,
                                        max_iterations = 50,
                                        conf_level = 0.95,
                                        tol = 0.001) {
  it <- 0
  diff_v <- NULL

  while (it < max_iterations) {
    it <- it + 1
    weights_new_t <- (1 + strata_qnorm^2 / n_per_strata)^2
    weights_new_b <- (vars + strata_qnorm^2 / (4 * n_per_strata^2))
    weights_new <- weights_new_t / weights_new_b
    weights_new <- weights_new / sum(weights_new)
    strata_qnorm <- strata_normal_quantile(vars, weights_new, conf_level)
    diff_v <- c(diff_v, sum(abs(weights_new - initial_weights)))
    if (diff_v[length(diff_v)] < tol) break
    initial_weights <- weights_new
  }

  if (it == max_iterations) {
    warning("The heuristic to find weights did not converge with max_iterations = ", max_iterations)
  }

  list(
    "n_it" = it,
    "weights" = weights_new,
    "diff_v" = diff_v
  )
}

#' Count Patients with Marked Laboratory Abnormalities
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Primary analysis variable `.var` indicates whether single, replicated or last marked laboratory
#' abnormality was observed (`factor`). Additional analysis variables are `id` (`character` or `factor`)
#' and `direction` (`factor`) indicating the direction of the abnormality. Denominator is number of
#' patients with at least one valid measurement during the analysis.
#'   * For `Single, not last` and `Last or replicated`: Numerator is number of patients
#'     with `Single, not last` and `Last or replicated` levels, respectively.
#'   * For `Any`: Numerator is the number of patients with either single or
#'     replicated marked abnormalities.
#'
#' @inheritParams argument_convention
#' @param category (`list`)\cr with different marked category names for single
#'   and last or replicated.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("abnormal_by_marked")`
#'   to see available statistics for this function.
#'
#' @note `Single, not last` and `Last or replicated` levels are mutually exclusive. If a patient has
#'   abnormalities that meet both the `Single, not last` and `Last or replicated` criteria, then the
#'   patient will be counted only under the `Last or replicated` category.
#'
#' @name abnormal_by_marked
#' @order 1
NULL

#' @describeIn abnormal_by_marked Statistics function for patients with marked lab abnormalities.
#'
#' @return
#' * `s_count_abnormal_by_marked()` returns statistic `count_fraction` with `Single, not last`,
#'   `Last or replicated`, and `Any` results.
#'
#' @keywords internal
s_count_abnormal_by_marked <- function(df,
                                       .var = "AVALCAT1",
                                       .spl_context,
                                       category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
                                       variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")) {
  checkmate::assert_string(.var)
  checkmate::assert_list(variables)
  checkmate::assert_list(category)
  checkmate::assert_subset(names(category), c("single", "last_replicated"))
  checkmate::assert_subset(names(variables), c("id", "param", "direction"))
  checkmate::assert_vector(unique(df[[variables$direction]]), max.len = 1)

  assert_df_with_variables(df, c(aval = .var, variables))
  checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))


  first_row <- .spl_context[.spl_context$split == variables[["param"]], ]
  # Patients in the denominator have at least one post-baseline visit.
  subj <- first_row$full_parent_df[[1]][[variables[["id"]]]]
  subj_cur_col <- subj[first_row$cur_col_subset[[1]]]
  # Some subjects may have a record for high and low directions but
  # should be counted only once.
  denom <- length(unique(subj_cur_col))

  if (denom != 0) {
    subjects_last_replicated <- unique(
      df[df[[.var]] %in% category[["last_replicated"]], variables$id, drop = TRUE]
    )
    subjects_single <- unique(
      df[df[[.var]] %in% category[["single"]], variables$id, drop = TRUE]
    )
    # Subjects who have both single and last/replicated abnormalities are counted in only the last/replicated group.
    subjects_single <- setdiff(subjects_single, subjects_last_replicated)
    n_single <- length(subjects_single)
    n_last_replicated <- length(subjects_last_replicated)
    n_any <- n_single + n_last_replicated
    result <- list(count_fraction = list(
      "Single, not last" = c(n_single, n_single / denom),
      "Last or replicated" = c(n_last_replicated, n_last_replicated / denom),
      "Any Abnormality" = c(n_any, n_any / denom)
    ))
  } else {
    result <- list(count_fraction = list(
      "Single, not last" = c(0, 0),
      "Last or replicated" = c(0, 0),
      "Any Abnormality" = c(0, 0)
    ))
  }

  result
}

#' @describeIn abnormal_by_marked Formatted analysis function which is used as `afun`
#'   in `count_abnormal_by_marked()`.
#'
#' @return
#' * `a_count_abnormal_by_marked()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_by_marked <- make_afun(
  s_count_abnormal_by_marked,
  .formats = c(count_fraction = format_count_fraction)
)

#' @describeIn abnormal_by_marked Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_by_marked()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal_by_marked()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))),
#'   ARMCD = factor(c(rep("ARM A", 5), rep("ARM B", 5), rep("ARM A", 5), rep("ARM B", 5))),
#'   ANRIND = factor(c(
#'     "NORMAL", "HIGH", "HIGH", "HIGH HIGH", "HIGH",
#'     "HIGH", "HIGH", "HIGH HIGH", "NORMAL", "HIGH HIGH", "NORMAL", "LOW", "LOW", "LOW LOW", "LOW",
#'     "LOW", "LOW", "LOW LOW", "NORMAL", "LOW LOW"
#'   )),
#'   ONTRTFL = rep(c("", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"), 2),
#'   PARAMCD = factor(c(rep("CRP", 10), rep("ALT", 10))),
#'   AVALCAT1 = factor(rep(c("", "", "", "SINGLE", "REPLICATED", "", "", "LAST", "", "SINGLE"), 2)),
#'   stringsAsFactors = FALSE
#' )
#'
#' df <- df %>%
#'   mutate(abn_dir = factor(
#'     case_when(
#'       ANRIND == "LOW LOW" ~ "Low",
#'       ANRIND == "HIGH HIGH" ~ "High",
#'       TRUE ~ ""
#'     ),
#'     levels = c("Low", "High")
#'   ))
#'
#' # Select only post-baseline records.
#' df <- df %>% filter(ONTRTFL == "Y")
#' df_crp <- df %>%
#'   filter(PARAMCD == "CRP") %>%
#'   droplevels()
#' full_parent_df <- list(df_crp, "not_needed")
#' cur_col_subset <- list(rep(TRUE, nrow(df_crp)), "not_needed")
#' spl_context <- data.frame(
#'   split = c("PARAMCD", "GRADE_DIR"),
#'   full_parent_df = I(full_parent_df),
#'   cur_col_subset = I(cur_col_subset)
#' )
#'
#' map <- unique(
#'   df[df$abn_dir %in% c("Low", "High") & df$AVALCAT1 != "", c("PARAMCD", "abn_dir")]
#' ) %>%
#'   lapply(as.character) %>%
#'   as.data.frame() %>%
#'   arrange(PARAMCD, abn_dir)
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   split_rows_by("PARAMCD") %>%
#'   summarize_num_patients(
#'     var = "USUBJID",
#'     .stats = "unique_count"
#'   ) %>%
#'   split_rows_by(
#'     "abn_dir",
#'     split_fun = trim_levels_to_map(map)
#'   ) %>%
#'   count_abnormal_by_marked(
#'     var = "AVALCAT1",
#'     variables = list(
#'       id = "USUBJID",
#'       param = "PARAMCD",
#'       direction = "abn_dir"
#'     )
#'   ) %>%
#'   build_table(df = df)
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   split_rows_by("PARAMCD") %>%
#'   summarize_num_patients(
#'     var = "USUBJID",
#'     .stats = "unique_count"
#'   ) %>%
#'   split_rows_by(
#'     "abn_dir",
#'     split_fun = trim_levels_in_group("abn_dir")
#'   ) %>%
#'   count_abnormal_by_marked(
#'     var = "AVALCAT1",
#'     variables = list(
#'       id = "USUBJID",
#'       param = "PARAMCD",
#'       direction = "abn_dir"
#'     )
#'   ) %>%
#'   build_table(df = df)
#'
#' @export
#' @order 2
count_abnormal_by_marked <- function(lyt,
                                     var,
                                     category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
                                     variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir"),
                                     na_str = default_na_str(),
                                     nested = TRUE,
                                     ...,
                                     .stats = NULL,
                                     .formats = NULL,
                                     .labels = NULL,
                                     .indent_mods = NULL) {
  checkmate::assert_string(var)

  extra_args <- list(category = category, variables = variables, ...)

  afun <- make_afun(
    a_count_abnormal_by_marked,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )

  lyt <- analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    show_labels = "hidden",
    extra_args = extra_args
  )
  lyt
}

#' Controls for Cox Regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Sets a list of parameters for Cox regression fit. Used internally.
#'
#' @inheritParams argument_convention
#' @param pval_method (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
#' @param interaction (`flag`)\cr if `TRUE`, the model includes the interaction between the studied
#'   treatment and candidate covariate. Note that for univariate models without treatment arm, and
#'   multivariate models, no interaction can be used so that this needs to be `FALSE`.
#' @param ties (`string`)\cr among `exact` (equivalent to `DISCRETE` in SAS), `efron` and `breslow`,
#'   see [survival::coxph()]. Note: there is no equivalent of SAS `EXACT` method in R.
#'
#' @return A `list` of items with names corresponding to the arguments.
#'
#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()].
#'
#' @examples
#' control_coxreg()
#'
#' @export
control_coxreg <- function(pval_method = c("wald", "likelihood"),
                           ties = c("exact", "efron", "breslow"),
                           conf_level = 0.95,
                           interaction = FALSE) {
  pval_method <- match.arg(pval_method)
  ties <- match.arg(ties)
  checkmate::assert_flag(interaction)
  assert_proportion_value(conf_level)
  list(
    pval_method = pval_method,
    ties = ties,
    conf_level = conf_level,
    interaction = interaction
  )
}

#' Custom Tidy Methods for Cox Regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @param x (`list`)\cr Result of the Cox regression model fitted by [fit_coxreg_univar()] (for univariate models)
#'   or [fit_coxreg_multivar()] (for multivariate models).
#'
#' @return [tidy()] returns:
#' * For `summary.coxph` objects,  a `data.frame` with columns: `Pr(>|z|)`, `exp(coef)`, `exp(-coef)`, `lower .95`,
#'   `upper .95`, `level`, and `n`.
#' * For `coxreg.univar` objects, a `data.frame` with columns: `effect`, `term`, `term_label`, `level`, `n`, `hr`,
#'   `lcl`, `ucl`, `pval`, and `ci`.
#' * For `coxreg.multivar` objects, a `data.frame` with columns: `term`, `pval`, `term_label`, `hr`, `lcl`, `ucl`,
#'   `level`, and `ci`.
#'
#' @seealso [cox_regression]
#'
#' @name tidy_coxreg
NULL

#' @describeIn tidy_coxreg Custom tidy method for [survival::coxph()] summary results.
#'
#' Tidy the [survival::coxph()] results into a `data.frame` to extract model results.
#'
#' @method tidy summary.coxph
#'
#' @examples
#' library(survival)
#' library(broom)
#'
#' set.seed(1, kind = "Mersenne-Twister")
#'
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   data.frame(
#'     time = stop,
#'     status = event,
#'     armcd = as.factor(rx),
#'     covar1 = as.factor(enum),
#'     covar2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4, labels = c("F", "F", "M", "M")
#'     )
#'   )
#' )
#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#'
#' formula <- "survival::Surv(time, status) ~ armcd + covar1"
#' msum <- summary(coxph(stats::as.formula(formula), data = dta_bladder))
#' tidy(msum)
#'
#' @export
tidy.summary.coxph <- function(x, # nolint
                               ...) {
  checkmate::assert_class(x, "summary.coxph")
  pval <- x$coefficients
  confint <- x$conf.int
  levels <- rownames(pval)

  pval <- tibble::as_tibble(pval)
  confint <- tibble::as_tibble(confint)

  ret <- cbind(pval[, grepl("Pr", names(pval))], confint)
  ret$level <- levels
  ret$n <- x[["n"]]
  ret
}

#' @describeIn tidy_coxreg Custom tidy method for a univariate Cox regression.
#'
#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_univar()].
#'
#' @method tidy coxreg.univar
#'
#' @examples
#' ## Cox regression: arm + 1 covariate.
#' mod1 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = "covar1"
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91)
#' )
#'
#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
#' mod2 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91, interaction = TRUE)
#' )
#'
#' tidy(mod1)
#' tidy(mod2)
#'
#' @export
tidy.coxreg.univar <- function(x, # nolint
                               ...) {
  checkmate::assert_class(x, "coxreg.univar")
  mod <- x$mod
  vars <- c(x$vars$arm, x$vars$covariates)
  has_arm <- "arm" %in% names(x$vars)

  result <- if (!has_arm) {
    Map(
      mod = mod, vars = vars,
      f = function(mod, vars) {
        h_coxreg_multivar_extract(
          var = vars,
          data = x$data,
          mod = mod,
          control = x$control
        )
      }
    )
  } else if (x$control$interaction) {
    Map(
      mod = mod, covar = vars,
      f = function(mod, covar) {
        h_coxreg_extract_interaction(
          effect = x$vars$arm, covar = covar, mod = mod, data = x$data,
          at = x$at, control = x$control
        )
      }
    )
  } else {
    Map(
      mod = mod, vars = vars,
      f = function(mod, vars) {
        h_coxreg_univar_extract(
          effect = x$vars$arm, covar = vars, data = x$data, mod = mod,
          control = x$control
        )
      }
    )
  }
  result <- do.call(rbind, result)

  result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
  result$n <- lapply(result$n, empty_vector_if_na)
  result$ci <- lapply(result$ci, empty_vector_if_na)
  result$hr <- lapply(result$hr, empty_vector_if_na)
  if (x$control$interaction) {
    result$pval_inter <- lapply(result$pval_inter, empty_vector_if_na)
    # Remove interaction p-values due to change in specifications.
    result$pval[result$effect != "Treatment:"] <- NA
  }
  result$pval <- lapply(result$pval, empty_vector_if_na)
  attr(result, "conf_level") <- x$control$conf_level
  result
}

#' @describeIn tidy_coxreg Custom tidy method for a multivariate Cox regression.
#'
#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_multivar()].
#'
#' @method tidy coxreg.multivar
#'
#' @examples
#' multivar_model <- fit_coxreg_multivar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#' broom::tidy(multivar_model)
#'
#' @export
tidy.coxreg.multivar <- function(x, # nolint
                                 ...) {
  checkmate::assert_class(x, "coxreg.multivar")
  vars <- c(x$vars$arm, x$vars$covariates)

  # Convert the model summaries to data.
  result <- Map(
    vars = vars,
    f = function(vars) {
      h_coxreg_multivar_extract(
        var = vars, data = x$data,
        mod = x$mod, control = x$control
      )
    }
  )
  result <- do.call(rbind, result)

  result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
  result$ci <- lapply(result$ci, empty_vector_if_na)
  result$hr <- lapply(result$hr, empty_vector_if_na)
  result$pval <- lapply(result$pval, empty_vector_if_na)
  result <- result[, names(result) != "n"]
  attr(result, "conf_level") <- x$control$conf_level

  result
}

#' Fits for Cox Proportional Hazards Regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Fitting functions for univariate and multivariate Cox regression models.
#'
#' @param variables (`list`)\cr a named list corresponds to the names of variables found in `data`, passed as a named
#'   list and corresponding to `time`, `event`, `arm`, `strata`, and `covariates` terms. If `arm` is missing from
#'   `variables`, then only Cox model(s) including the `covariates` will be fitted and the corresponding effect
#'   estimates will be tabulated later.
#' @param data (`data.frame`)\cr the dataset containing the variables to fit the models.
#' @param at (`list` of `numeric`)\cr when the candidate covariate is a `numeric`, use `at` to specify
#'   the value of the covariate at which the effect should be estimated.
#' @param control (`list`)\cr a list of parameters as returned by the helper function [control_coxreg()].
#'
#' @seealso [h_cox_regression] for relevant helper functions, [cox_regression].
#'
#' @examples
#' library(survival)
#'
#' set.seed(1, kind = "Mersenne-Twister")
#'
#' # Testing dataset [survival::bladder].
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   data.frame(
#'     time = stop,
#'     status = event,
#'     armcd = as.factor(rx),
#'     covar1 = as.factor(enum),
#'     covar2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4, labels = c("F", "F", "M", "M")
#'     )
#'   )
#' )
#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#'
#' plot(
#'   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
#'   lty = 2:4,
#'   xlab = "Months",
#'   col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
#' )
#'
#' @name fit_coxreg
NULL

#' @describeIn fit_coxreg Fit a series of univariate Cox regression models given the inputs.
#'
#' @return
#' * `fit_coxreg_univar()` returns a `coxreg.univar` class object which is a named `list`
#'   with 5 elements:
#'   * `mod`: Cox regression models fitted by [survival::coxph()].
#'   * `data`: The original data frame input.
#'   * `control`: The original control input.
#'   * `vars`: The variables used in the model.
#'   * `at`: Value of the covariate at which the effect should be estimated.
#'
#' @note When using `fit_coxreg_univar` there should be two study arms.
#'
#' @examples
#' # fit_coxreg_univar
#'
#' ## Cox regression: arm + 1 covariate.
#' mod1 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = "covar1"
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91)
#' )
#'
#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
#' mod2 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91, interaction = TRUE)
#' )
#'
#' ## Cox regression: arm + 1 covariate, stratified analysis.
#' mod3 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", strata = "covar2",
#'     covariates = c("covar1")
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91)
#' )
#'
#' ## Cox regression: no arm, only covariates.
#' mod4 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#'
#' @export
fit_coxreg_univar <- function(variables,
                              data,
                              at = list(),
                              control = control_coxreg()) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  assert_df_with_variables(data, variables)
  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  if (!is.null(variables$strata)) {
    checkmate::assert_disjunct(control$pval_method, "likelihood")
  }
  if (has_arm) {
    assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  }
  vars <- unlist(variables[c(arm_name, "covariates", "strata")], use.names = FALSE)
  for (i in vars) {
    if (is.factor(data[[i]])) {
      attr(data[[i]], "levels") <- levels(droplevels(data[[i]]))
    }
  }
  forms <- h_coxreg_univar_formulas(variables, interaction = control$interaction)
  mod <- lapply(
    forms, function(x) {
      survival::coxph(formula = stats::as.formula(x), data = data, ties = control$ties)
    }
  )
  structure(
    list(
      mod = mod,
      data = data,
      control = control,
      vars = variables,
      at = at
    ),
    class = "coxreg.univar"
  )
}

#' @describeIn fit_coxreg Fit a multivariate Cox regression model.
#'
#' @return
#' * `fit_coxreg_multivar()` returns a `coxreg.multivar` class object which is a named list
#'   with 4 elements:
#'   * `mod`: Cox regression model fitted by [survival::coxph()].
#'   * `data`: The original data frame input.
#'   * `control`: The original control input.
#'   * `vars`: The variables used in the model.
#'
#' @examples
#' # fit_coxreg_multivar
#'
#' ## Cox regression: multivariate Cox regression.
#' multivar_model <- fit_coxreg_multivar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#'
#' # Example without treatment arm.
#' multivar_covs_model <- fit_coxreg_multivar(
#'   variables = list(
#'     time = "time", event = "status",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#'
#' @export
fit_coxreg_multivar <- function(variables,
                                data,
                                control = control_coxreg()) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  if (!is.null(variables$covariates)) {
    checkmate::assert_character(variables$covariates)
  }

  checkmate::assert_false(control$interaction)
  assert_df_with_variables(data, variables)
  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  if (!is.null(variables$strata)) {
    checkmate::assert_disjunct(control$pval_method, "likelihood")
  }

  form <- h_coxreg_multivar_formula(variables)
  mod <- survival::coxph(
    formula = stats::as.formula(form),
    data = data,
    ties = control$ties
  )
  structure(
    list(
      mod = mod,
      data = data,
      control = control,
      vars = variables
    ),
    class = "coxreg.multivar"
  )
}

#' Muffled `car::Anova`
#'
#' Applied on survival models, [car::Anova()] signal that the `strata` terms is dropped from the model formula when
#' present, this function deliberately muffles this message.
#'
#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
#' @param test_statistic (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
#'
#' @return Returns the output of [car::Anova()], with convergence message muffled.
#'
#' @keywords internal
muffled_car_anova <- function(mod, test_statistic) {
  tryCatch(
    withCallingHandlers(
      expr = {
        car::Anova(
          mod,
          test.statistic = test_statistic,
          type = "III"
        )
      },
      message = function(m) invokeRestart("muffleMessage"),
      error = function(e) {
        stop(paste(
          "the model seems to have convergence problems, please try to change",
          "the configuration of covariates or strata variables, e.g.",
          "- original error:", e
        ))
      }
    )
  )
}

#' Kaplan-Meier Plot
#'
#' @description `r lifecycle::badge("stable")`
#'
#' From a survival model, a graphic is rendered along with tabulated annotation
#' including the number of patient at risk at given time and the median survival
#' per group.
#'
#' @inheritParams grid::gTree
#' @inheritParams argument_convention
#' @param df (`data.frame`)\cr data set containing all analysis variables.
#' @param variables (named `list`)\cr variable names. Details are:
#'   * `tte` (`numeric`)\cr variable indicating time-to-event duration values.
#'   * `is_event` (`logical`)\cr event variable. `TRUE` if event, `FALSE` if time to event is censored.
#'   * `arm` (`factor`)\cr the treatment group variable.
#'   * `strata` (`character` or `NULL`)\cr variable names indicating stratification factors.
#' @param control_surv (`list`)\cr parameters for comparison details, specified by using
#'   the helper function [control_surv_timepoint()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for survival rate.
#'   * `conf_type` (`string`)\cr `"plain"` (default), `"log"`, `"log-log"` for confidence interval type,
#'     see more in [survival::survfit()]. Note that the option "none" is no longer supported.
#' @param xticks (`numeric`, `number`, or `NULL`)\cr numeric vector of ticks or single number with spacing
#'   between ticks on the x axis. If `NULL` (default), [labeling::extended()] is used to determine
#'   an optimal tick position on the x axis.
#' @param yval (`string`)\cr value of y-axis. Options are `Survival` (default) and `Failure` probability.
#' @param censor_show (`flag`)\cr whether to show censored.
#' @param xlab (`string`)\cr label of x-axis.
#' @param ylab (`string`)\cr label of y-axis.
#' @param ylim (`vector` of `numeric`)\cr vector of length 2 containing lower and upper limits for the y-axis.
#'   If `NULL` (default), the minimum and maximum y-values displayed are used as limits.
#' @param title (`string`)\cr title for plot.
#' @param footnotes (`string`)\cr footnotes for plot.
#' @param col (`character`)\cr lines colors. Length of a vector should be equal
#'   to number of strata from [survival::survfit()].
#' @param lty (`numeric`)\cr line type. Length of a vector should be equal
#'   to number of strata from [survival::survfit()].
#' @param lwd (`numeric`)\cr line width. Length of a vector should be equal
#'   to number of strata from [survival::survfit()].
#' @param pch (`numeric`, `string`)\cr value or character of points symbol to indicate censored cases.
#' @param size (`numeric`)\cr size of censored point, a class of `unit`.
#' @param max_time (`numeric`)\cr maximum value to show on X axis. Only data values less than or up to
#'   this threshold value will be plotted (defaults to `NULL`).
#' @param font_size (`number`)\cr font size to be used.
#' @param ci_ribbon (`flag`)\cr draw the confidence interval around the Kaplan-Meier curve.
#' @param ggtheme (`theme`)\cr a graphical theme as provided by `ggplot2` to control outlook of the Kaplan-Meier curve.
#' @param annot_at_risk (`flag`)\cr compute and add the annotation table reporting the number of patient at risk
#'   matching the main grid of the Kaplan-Meier curve.
#' @param annot_at_risk_title (`flag`)\cr whether the "Patients at Risk" title should be added above the `annot_at_risk`
#'   table. Has no effect if `annot_at_risk` is `FALSE`. Defaults to `TRUE`.
#' @param annot_surv_med (`flag`)\cr compute and add the annotation table on the Kaplan-Meier curve estimating the
#'   median survival time per group.
#' @param annot_coxph (`flag`)\cr add the annotation table from a [survival::coxph()] model.
#' @param annot_stats (`string`)\cr statistics annotations to add to the plot. Options are
#'   `median` (median survival follow-up time) and `min` (minimum survival follow-up time).
#' @param annot_stats_vlines (`flag`)\cr add vertical lines corresponding to each of the statistics
#'   specified by `annot_stats`. If `annot_stats` is `NULL` no lines will be added.
#' @param control_coxph_pw (`list`)\cr parameters for comparison details, specified by using
#'   the helper function [control_coxph()]. Some possible parameter options are:
#'   * `pval_method` (`string`)\cr p-value method for testing hazard ratio = 1.
#'     Default method is `"log-rank"`, can also be set to `"wald"` or `"likelihood"`.
#'   * `ties` (`string`)\cr method for tie handling. Default is `"efron"`,
#'     can also be set to `"breslow"` or `"exact"`. See more in [survival::coxph()]
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for HR.
#' @param ref_group_coxph (`character`)\cr level of arm variable to use as reference group in calculations for
#'   `annot_coxph` table. If `NULL` (default), uses the first level of the arm variable.
#' @param annot_coxph_ref_lbls (`flag`)\cr whether the reference group should be explicitly printed in labels for the
#'   `annot_coxph` table. If `FALSE` (default), only comparison groups will be printed in `annot_coxph` table labels.
#' @param position_coxph (`numeric`)\cr x and y positions for plotting [survival::coxph()] model.
#' @param position_surv_med (`numeric`)\cr x and y positions for plotting annotation table estimating median survival
#'   time per group.
#' @param width_annots (named `list` of `unit`s)\cr a named list of widths for annotation tables with names `surv_med`
#'   (median survival time table) and `coxph` ([survival::coxph()] model table), where each value is the width
#'   (in units) to implement when printing the annotation table.
#'
#' @return A `grob` of class `gTree`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(ggplot2)
#' library(survival)
#' library(grid)
#' library(nestcolor)
#'
#' df <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#' variables <- list(tte = "AVAL", is_event = "is_event", arm = "ARMCD")
#'
#' # 1. Example - basic option
#'
#' res <- g_km(df = df, variables = variables)
#' res <- g_km(df = df, variables = variables, yval = "Failure")
#' res <- g_km(
#'   df = df,
#'   variables = variables,
#'   control_surv = control_surv_timepoint(conf_level = 0.9),
#'   col = c("grey25", "grey50", "grey75"),
#'   annot_at_risk_title = FALSE
#' )
#' res <- g_km(df = df, variables = variables, ggtheme = theme_minimal())
#' res <- g_km(df = df, variables = variables, ggtheme = theme_minimal(), lty = 1:3)
#' res <- g_km(df = df, variables = variables, max_time = 2000)
#' res <- g_km(
#'   df = df,
#'   variables = variables,
#'   annot_stats = c("min", "median"),
#'   annot_stats_vlines = TRUE
#' )
#'
#' # 2. Example - Arrange several KM curve on a single graph device
#'
#' # 2.1 Use case: A general graph on the top, a zoom on the bottom.
#' grid.newpage()
#' lyt <- grid.layout(nrow = 2, ncol = 1) %>%
#'   viewport(layout = .) %>%
#'   pushViewport()
#'
#' res <- g_km(
#'   df = df,
#'   variables = variables,
#'   newpage = FALSE,
#'   annot_surv_med = FALSE,
#'   vp = viewport(layout.pos.row = 1, layout.pos.col = 1)
#' )
#' res <- g_km(
#'   df = df,
#'   variables = variables,
#'   max_time = 1000,
#'   newpage = FALSE,
#'   annot_surv_med = FALSE,
#'   ggtheme = theme_dark(),
#'   vp = viewport(layout.pos.row = 2, layout.pos.col = 1)
#' )
#'
#' # 2.1 Use case: No annotations on top, annotated graph on bottom
#' grid.newpage()
#' lyt <- grid.layout(nrow = 2, ncol = 1) %>%
#'   viewport(layout = .) %>%
#'   pushViewport()
#'
#' res <- g_km(
#'   df = df,
#'   variables = variables,
#'   newpage = FALSE,
#'   annot_surv_med = FALSE,
#'   annot_at_risk = FALSE,
#'   vp = viewport(layout.pos.row = 1, layout.pos.col = 1)
#' )
#' res <- g_km(
#'   df = df,
#'   variables = variables,
#'   max_time = 2000,
#'   newpage = FALSE,
#'   annot_surv_med = FALSE,
#'   annot_at_risk = TRUE,
#'   ggtheme = theme_dark(),
#'   vp = viewport(layout.pos.row = 2, layout.pos.col = 1)
#' )
#'
#' # Add annotation from a pairwise coxph analysis
#' g_km(
#'   df = df,
#'   variables = variables,
#'   annot_coxph = TRUE
#' )
#'
#' # Change widths/sizes of surv_med and coxph annotation tables.
#' g_km(
#'   df = df,
#'   variables = c(variables, list(strata = "SEX")),
#'   annot_coxph = TRUE,
#'   width_annots = list(surv_med = grid::unit(2, "in"), coxph = grid::unit(3, "in"))
#' )
#'
#' g_km(
#'   df = df,
#'   variables = c(variables, list(strata = "SEX")),
#'   font_size = 15,
#'   annot_coxph = TRUE,
#'   control_coxph_pw = control_coxph(pval_method = "wald", ties = "exact", conf_level = 0.99),
#'   position_coxph = c(0.5, 0.5)
#' )
#'
#' # Change position of the treatment group annotation table.
#' g_km(
#'   df = df,
#'   variables = c(variables, list(strata = "SEX")),
#'   font_size = 15,
#'   annot_coxph = TRUE,
#'   control_coxph_pw = control_coxph(pval_method = "wald", ties = "exact", conf_level = 0.99),
#'   position_surv_med = c(1, 0.7)
#' )
#' }
#'
#' @export
g_km <- function(df,
                 variables,
                 control_surv = control_surv_timepoint(),
                 col = NULL,
                 lty = NULL,
                 lwd = .5,
                 censor_show = TRUE,
                 pch = 3,
                 size = 2,
                 max_time = NULL,
                 xticks = NULL,
                 xlab = "Days",
                 yval = c("Survival", "Failure"),
                 ylab = paste(yval, "Probability"),
                 ylim = NULL,
                 title = NULL,
                 footnotes = NULL,
                 draw = TRUE,
                 newpage = TRUE,
                 gp = NULL,
                 vp = NULL,
                 name = NULL,
                 font_size = 12,
                 ci_ribbon = FALSE,
                 ggtheme = nestcolor::theme_nest(),
                 annot_at_risk = TRUE,
                 annot_at_risk_title = TRUE,
                 annot_surv_med = TRUE,
                 annot_coxph = FALSE,
                 annot_stats = NULL,
                 annot_stats_vlines = FALSE,
                 control_coxph_pw = control_coxph(),
                 ref_group_coxph = NULL,
                 annot_coxph_ref_lbls = FALSE,
                 position_coxph = c(-0.03, -0.02),
                 position_surv_med = c(0.95, 0.9),
                 width_annots = list(surv_med = grid::unit(0.3, "npc"), coxph = grid::unit(0.4, "npc"))) {
  checkmate::assert_list(variables)
  checkmate::assert_subset(c("tte", "arm", "is_event"), names(variables))
  checkmate::assert_string(title, null.ok = TRUE)
  checkmate::assert_string(footnotes, null.ok = TRUE)
  checkmate::assert_character(col, null.ok = TRUE)
  checkmate::assert_subset(annot_stats, c("median", "min"))
  checkmate::assert_logical(annot_stats_vlines)
  checkmate::assert_true(all(sapply(width_annots, grid::is.unit)))

  tte <- variables$tte
  is_event <- variables$is_event
  arm <- variables$arm

  assert_valid_factor(df[[arm]])
  assert_df_with_variables(df, list(tte = tte, is_event = is_event, arm = arm))
  checkmate::assert_logical(df[[is_event]], min.len = 1, any.missing = FALSE)
  checkmate::assert_numeric(df[[tte]], min.len = 1, any.missing = FALSE)

  armval <- as.character(unique(df[[arm]]))
  if (annot_coxph && length(armval) < 2) {
    stop(paste(
      "When `annot_coxph` = TRUE, `df` must contain at least 2 levels of `variables$arm`",
      "in order to calculate the hazard ratio."
    ))
  } else if (length(armval) > 1) {
    armval <- NULL
  }
  yval <- match.arg(yval)
  formula <- stats::as.formula(paste0("survival::Surv(", tte, ", ", is_event, ") ~ ", arm))
  fit_km <- survival::survfit(
    formula = formula,
    data = df,
    conf.int = control_surv$conf_level,
    conf.type = control_surv$conf_type
  )
  data_plot <- h_data_plot(
    fit_km = fit_km,
    armval = armval,
    max_time = max_time
  )

  xticks <- h_xticks(data = data_plot, xticks = xticks, max_time = max_time)
  gg <- h_ggkm(
    data = data_plot,
    censor_show = censor_show,
    pch = pch,
    size = size,
    xticks = xticks,
    xlab = xlab,
    yval = yval,
    ylab = ylab,
    ylim = ylim,
    title = title,
    footnotes = footnotes,
    max_time = max_time,
    lwd = lwd,
    lty = lty,
    col = col,
    ggtheme = ggtheme,
    ci_ribbon = ci_ribbon
  )

  if (!is.null(annot_stats)) {
    if ("median" %in% annot_stats) {
      fit_km_all <- survival::survfit(
        formula = stats::as.formula(paste0("survival::Surv(", tte, ", ", is_event, ") ~ ", 1)),
        data = df,
        conf.int = control_surv$conf_level,
        conf.type = control_surv$conf_type
      )
      gg <- gg +
        geom_text(
          size = 8 / ggplot2::.pt, col = 1,
          x = stats::median(fit_km_all) + 0.065 * max(data_plot$time),
          y = ifelse(yval == "Survival", 0.62, 0.38),
          label = paste("Median F/U:\n", round(stats::median(fit_km_all), 1), tolower(df$AVALU[1]))
        )
      if (annot_stats_vlines) {
        gg <- gg +
          geom_segment(aes(x = stats::median(fit_km_all), xend = stats::median(fit_km_all), y = -Inf, yend = Inf),
            linetype = 2, col = "darkgray"
          )
      }
    }
    if ("min" %in% annot_stats) {
      min_fu <- min(df[[tte]])
      gg <- gg +
        geom_text(
          size = 8 / ggplot2::.pt, col = 1,
          x = min_fu + max(data_plot$time) * ifelse(yval == "Survival", 0.05, 0.07),
          y = ifelse(yval == "Survival", 1.0, 0.05),
          label = paste("Min. F/U:\n", round(min_fu, 1), tolower(df$AVALU[1]))
        )
      if (annot_stats_vlines) {
        gg <- gg +
          geom_segment(aes(x = min_fu, xend = min_fu, y = Inf, yend = -Inf), linetype = 2, col = "darkgray")
      }
    }
    gg <- gg + ggplot2::guides(fill = ggplot2::guide_legend(override.aes = list(shape = NA, label = "")))
  }

  g_el <- h_decompose_gg(gg)

  if (annot_at_risk) {
    # This is the content of the table that will be below the graph.
    annot_tbl <- summary(fit_km, times = xticks)
    annot_tbl <- if (is.null(fit_km$strata)) {
      data.frame(
        n.risk = annot_tbl$n.risk,
        time = annot_tbl$time,
        strata = as.factor(armval)
      )
    } else {
      strata_lst <- strsplit(sub("=", "equals", levels(annot_tbl$strata)), "equals")
      levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]
      data.frame(
        n.risk = annot_tbl$n.risk,
        time = annot_tbl$time,
        strata = annot_tbl$strata
      )
    }

    grobs_patient <- h_grob_tbl_at_risk(
      data = data_plot,
      annot_tbl = annot_tbl,
      xlim = max(max_time, data_plot$time, xticks),
      title = annot_at_risk_title
    )
  }

  if (annot_at_risk || annot_surv_med || annot_coxph) {
    lyt <- h_km_layout(
      data = data_plot, g_el = g_el, title = title, footnotes = footnotes,
      annot_at_risk = annot_at_risk, annot_at_risk_title = annot_at_risk_title
    )
    at_risk_ttl <- as.numeric(annot_at_risk_title)
    ttl_row <- as.numeric(!is.null(title))
    foot_row <- as.numeric(!is.null(footnotes))
    km_grob <- grid::gTree(
      vp = grid::viewport(layout = lyt, height = .95, width = .95),
      children = grid::gList(
        # Title.
        if (ttl_row == 1) {
          grid::gTree(
            vp = grid::viewport(layout.pos.row = 1, layout.pos.col = 2),
            children = grid::gList(grid::textGrob(label = title, x = grid::unit(0, "npc"), hjust = 0))
          )
        },

        # The Kaplan - Meier curve (top-right corner).
        grid::gTree(
          vp = grid::viewport(layout.pos.row = 1 + ttl_row, layout.pos.col = 2),
          children = grid::gList(g_el$panel)
        ),

        # Survfit summary table (top-right corner).
        if (annot_surv_med) {
          grid::gTree(
            vp = grid::viewport(layout.pos.row = 1 + ttl_row, layout.pos.col = 2),
            children = h_grob_median_surv(
              fit_km = fit_km,
              armval = armval,
              x = position_surv_med[1],
              y = position_surv_med[2],
              width = if (!is.null(width_annots[["surv_med"]])) width_annots[["surv_med"]] else grid::unit(0.3, "npc"),
              ttheme = gridExtra::ttheme_default(base_size = font_size)
            )
          )
        },
        if (annot_coxph) {
          grid::gTree(
            vp = grid::viewport(layout.pos.row = 1 + ttl_row, layout.pos.col = 2),
            children = h_grob_coxph(
              df = df,
              variables = variables,
              control_coxph_pw = control_coxph_pw,
              ref_group_coxph = ref_group_coxph,
              annot_coxph_ref_lbls = annot_coxph_ref_lbls,
              x = position_coxph[1],
              y = position_coxph[2],
              width = if (!is.null(width_annots[["coxph"]])) width_annots[["coxph"]] else grid::unit(0.4, "npc"),
              ttheme = gridExtra::ttheme_default(
                base_size = font_size,
                padding = grid::unit(c(1, .5), "lines"),
                core = list(bg_params = list(fill = c("grey95", "grey90"), alpha = .5))
              )
            )
          )
        },

        # Add the y-axis annotation (top-left corner).
        grid::gTree(
          vp = grid::viewport(layout.pos.row = 1 + ttl_row, layout.pos.col = 1),
          children = h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis)
        ),

        # Add the x-axis annotation (second row below the Kaplan Meier Curve).
        grid::gTree(
          vp = grid::viewport(layout.pos.row = 2 + ttl_row, layout.pos.col = 2),
          children = grid::gList(rbind(g_el$xaxis, g_el$xlab))
        ),

        # Add the legend.
        grid::gTree(
          vp = grid::viewport(layout.pos.row = 3 + ttl_row, layout.pos.col = 2),
          children = grid::gList(g_el$guide)
        ),

        # Add the table with patient-at-risk numbers.
        if (annot_at_risk && annot_at_risk_title) {
          grid::gTree(
            vp = grid::viewport(layout.pos.row = 4 + ttl_row, layout.pos.col = 1),
            children = grobs_patient$title
          )
        },
        if (annot_at_risk) {
          grid::gTree(
            vp = grid::viewport(layout.pos.row = 4 + at_risk_ttl + ttl_row, layout.pos.col = 2),
            children = grobs_patient$at_risk
          )
        },
        if (annot_at_risk) {
          grid::gTree(
            vp = grid::viewport(layout.pos.row = 4 + at_risk_ttl + ttl_row, layout.pos.col = 1),
            children = grobs_patient$label
          )
        },
        if (annot_at_risk) {
          # Add the x-axis for the table.
          grid::gTree(
            vp = grid::viewport(layout.pos.row = 5 + at_risk_ttl + ttl_row, layout.pos.col = 2),
            children = grid::gList(rbind(g_el$xaxis, g_el$xlab))
          )
        },

        # Footnotes.
        if (foot_row == 1) {
          grid::gTree(
            vp = grid::viewport(
              layout.pos.row = ifelse(annot_at_risk, 6 + at_risk_ttl + ttl_row, 4 + ttl_row),
              layout.pos.col = 2
            ),
            children = grid::gList(grid::textGrob(label = footnotes, x = grid::unit(0, "npc"), hjust = 0))
          )
        }
      )
    )

    result <- grid::gTree(
      vp = vp,
      gp = gp,
      name = name,
      children = grid::gList(km_grob)
    )
  } else {
    result <- grid::gTree(
      vp = vp,
      gp = gp,
      name = name,
      children = grid::gList(ggplot2::ggplotGrob(gg))
    )
  }

  if (newpage && draw) grid::grid.newpage()
  if (draw) grid::grid.draw(result)
  invisible(result)
}

#' Helper function: tidy survival fit
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convert the survival fit data into a data frame designed for plotting
#' within `g_km`.
#'
#' This starts from the [broom::tidy()] result, and then:
#'   * Post-processes the `strata` column into a factor.
#'   * Extends each stratum by an additional first row with time 0 and probability 1 so that
#'     downstream plot lines start at those coordinates.
#'   * Adds a `censor` column.
#'   * Filters the rows before `max_time`.
#'
#' @inheritParams g_km
#' @param fit_km (`survfit`)\cr result of [survival::survfit()].
#' @param armval (`string`)\cr used as strata name when treatment arm variable only has one level. Default is `"All"`.
#'
#' @return A `tibble` with columns `time`, `n.risk`, `n.event`, `n.censor`, `estimate`, `std.error`, `conf.high`,
#'   `conf.low`, `strata`, and `censor`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#'
#' # Test with multiple arms
#' tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_data_plot()
#'
#' # Test with single arm
#' tern_ex_adtte %>%
#'   filter(PARAMCD == "OS", ARMCD == "ARM B") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_data_plot(armval = "ARM B")
#' }
#'
#' @export
h_data_plot <- function(fit_km,
                        armval = "All",
                        max_time = NULL) {
  y <- broom::tidy(fit_km)

  if (!is.null(fit_km$strata)) {
    fit_km_var_level <- strsplit(sub("=", "equals", names(fit_km$strata)), "equals")
    strata_levels <- vapply(fit_km_var_level, FUN = "[", FUN.VALUE = "a", i = 2)
    strata_var_level <- strsplit(sub("=", "equals", y$strata), "equals")
    y$strata <- factor(
      vapply(strata_var_level, FUN = "[", FUN.VALUE = "a", i = 2),
      levels = strata_levels
    )
  } else {
    y$strata <- armval
  }

  y_by_strata <- split(y, y$strata)
  y_by_strata_extended <- lapply(
    y_by_strata,
    FUN = function(tbl) {
      first_row <- tbl[1L, ]
      first_row$time <- 0
      first_row$n.risk <- sum(first_row[, c("n.risk", "n.event", "n.censor")])
      first_row$n.event <- first_row$n.censor <- 0
      first_row$estimate <- first_row$conf.high <- first_row$conf.low <- 1
      first_row$std.error <- 0
      rbind(
        first_row,
        tbl
      )
    }
  )
  y <- do.call(rbind, y_by_strata_extended)

  y$censor <- ifelse(y$n.censor > 0, y$estimate, NA)
  if (!is.null(max_time)) {
    y <- y[y$time <= max(max_time), ]
  }
  y
}

#' Helper function: x tick positions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Calculate the positions of ticks on the x-axis. However, if `xticks` already
#' exists it is kept as is. It is based on the same function `ggplot2` relies on,
#' and is required in the graphic and the patient-at-risk annotation table.
#'
#' @inheritParams g_km
#' @inheritParams h_ggkm
#'
#' @return A vector of positions to use for x-axis ticks on a `ggplot` object.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#'
#' data <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_data_plot()
#'
#' h_xticks(data)
#' h_xticks(data, xticks = seq(0, 3000, 500))
#' h_xticks(data, xticks = 500)
#' h_xticks(data, xticks = 500, max_time = 6000)
#' h_xticks(data, xticks = c(0, 500), max_time = 300)
#' h_xticks(data, xticks = 500, max_time = 300)
#' }
#'
#' @export
h_xticks <- function(data, xticks = NULL, max_time = NULL) {
  if (is.null(xticks)) {
    if (is.null(max_time)) {
      labeling::extended(range(data$time)[1], range(data$time)[2], m = 5)
    } else {
      labeling::extended(range(data$time)[1], max(range(data$time)[2], max_time), m = 5)
    }
  } else if (checkmate::test_number(xticks)) {
    if (is.null(max_time)) {
      seq(0, max(data$time), xticks)
    } else {
      seq(0, max(data$time, max_time), xticks)
    }
  } else if (is.numeric(xticks)) {
    xticks
  } else {
    stop(
      paste(
        "xticks should be either `NULL`",
        "or a single number (interval between x ticks)",
        "or a numeric vector (position of ticks on the x axis)"
      )
    )
  }
}

#' Helper function: KM plot
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Draw the Kaplan-Meier plot using `ggplot2`.
#'
#' @inheritParams g_km
#' @param data (`data.frame`)\cr survival data as pre-processed by `h_data_plot`.
#'
#' @return A `ggplot` object.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks,
#'   xlab = "Days",
#'   yval = "Survival",
#'   ylab = "Survival Probability",
#'   title = "Survival"
#' )
#' gg
#' }
#'
#' @export
h_ggkm <- function(data,
                   xticks = NULL,
                   yval = "Survival",
                   censor_show,
                   xlab,
                   ylab,
                   ylim = NULL,
                   title,
                   footnotes = NULL,
                   max_time = NULL,
                   lwd = 1,
                   lty = NULL,
                   pch = 3,
                   size = 2,
                   col = NULL,
                   ci_ribbon = FALSE,
                   ggtheme = nestcolor::theme_nest()) {
  checkmate::assert_numeric(lty, null.ok = TRUE)
  checkmate::assert_character(col, null.ok = TRUE)

  if (is.null(ylim)) {
    data_lims <- data
    if (yval == "Failure") data_lims[["estimate"]] <- 1 - data_lims[["estimate"]]
    if (!is.null(max_time)) {
      y_lwr <- min(data_lims[data_lims$time < max_time, ][["estimate"]])
      y_upr <- max(data_lims[data_lims$time < max_time, ][["estimate"]])
    } else {
      y_lwr <- min(data_lims[["estimate"]])
      y_upr <- max(data_lims[["estimate"]])
    }
    ylim <- c(y_lwr, y_upr)
  }
  checkmate::assert_numeric(ylim, finite = TRUE, any.missing = FALSE, len = 2, sorted = TRUE)

  # change estimates of survival to estimates of failure (1 - survival)
  if (yval == "Failure") {
    data$estimate <- 1 - data$estimate
    data[c("conf.high", "conf.low")] <- list(1 - data$conf.low, 1 - data$conf.high)
    data$censor <- 1 - data$censor
  }

  gg <- {
    ggplot2::ggplot(
      data = data,
      mapping = ggplot2::aes(
        x = .data[["time"]],
        y = .data[["estimate"]],
        ymin = .data[["conf.low"]],
        ymax = .data[["conf.high"]],
        color = .data[["strata"]],
        fill = .data[["strata"]]
      )
    ) +
      ggplot2::geom_hline(yintercept = 0)
  }

  if (ci_ribbon) {
    gg <- gg + ggplot2::geom_ribbon(alpha = .3, lty = 0)
  }

  gg <- if (is.null(lty)) {
    gg +
      ggplot2::geom_step(linewidth = lwd)
  } else if (checkmate::test_number(lty)) {
    gg +
      ggplot2::geom_step(linewidth = lwd, lty = lty)
  } else if (is.numeric(lty)) {
    gg +
      ggplot2::geom_step(mapping = ggplot2::aes(linetype = .data[["strata"]]), linewidth = lwd) +
      ggplot2::scale_linetype_manual(values = lty)
  }

  gg <- gg +
    ggplot2::coord_cartesian(ylim = ylim) +
    ggplot2::labs(x = xlab, y = ylab, title = title, caption = footnotes)

  if (!is.null(col)) {
    gg <- gg +
      ggplot2::scale_color_manual(values = col) +
      ggplot2::scale_fill_manual(values = col)
  }
  if (censor_show) {
    dt <- data[data$n.censor != 0, ]
    dt$censor_lbl <- factor("Censored")

    gg <- gg + ggplot2::geom_point(
      data = dt,
      ggplot2::aes(
        x = .data[["time"]],
        y = .data[["censor"]],
        shape = .data[["censor_lbl"]]
      ),
      size = size,
      show.legend = TRUE,
      inherit.aes = TRUE
    ) +
      ggplot2::scale_shape_manual(name = NULL, values = pch) +
      ggplot2::guides(
        shape = ggplot2::guide_legend(override.aes = list(linetype = NA)),
        fill = ggplot2::guide_legend(override.aes = list(shape = NA))
      )
  }

  if (!is.null(max_time) && !is.null(xticks)) {
    gg <- gg + ggplot2::scale_x_continuous(breaks = xticks, limits = c(min(0, xticks), max(c(xticks, max_time))))
  } else if (!is.null(xticks)) {
    if (max(data$time) <= max(xticks)) {
      gg <- gg + ggplot2::scale_x_continuous(breaks = xticks, limits = c(min(0, min(xticks)), max(xticks)))
    } else {
      gg <- gg + ggplot2::scale_x_continuous(breaks = xticks)
    }
  } else if (!is.null(max_time)) {
    gg <- gg + ggplot2::scale_x_continuous(limits = c(0, max_time))
  }

  if (!is.null(ggtheme)) {
    gg <- gg + ggtheme
  }

  gg + ggplot2::theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    legend.key.height = unit(0.02, "npc"),
    panel.grid.major.x = ggplot2::element_line(linewidth = 2)
  )
}

#' `ggplot` Decomposition
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The elements composing the `ggplot` are extracted and organized in a `list`.
#'
#' @param gg (`ggplot`)\cr a graphic to decompose.
#'
#' @return A named `list` with elements:
#'   * `panel`: The panel.
#'   * `yaxis`: The y-axis.
#'   * `xaxis`: The x-axis.
#'   * `xlab`: The x-axis label.
#'   * `ylab`: The y-axis label.
#'   * `guide`: The legend.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   yval = "Survival",
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "tt",
#'   footnotes = "ff"
#' )
#'
#' g_el <- h_decompose_gg(gg)
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "red", fill = "gray85", lwd = 5))
#' grid::grid.draw(g_el$panel)
#'
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "royalblue", fill = "gray85", lwd = 5))
#' grid::grid.draw(with(g_el, cbind(ylab, yaxis)))
#' }
#'
#' @export
h_decompose_gg <- function(gg) {
  g_el <- ggplot2::ggplotGrob(gg)
  y <- c(
    panel = "panel",
    yaxis = "axis-l",
    xaxis = "axis-b",
    xlab = "xlab-b",
    ylab = "ylab-l",
    guide = "guide"
  )
  lapply(X = y, function(x) gtable::gtable_filter(g_el, x))
}

#' Helper: KM Layout
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares a (5 rows) x (2 cols) layout for the Kaplan-Meier curve.
#'
#' @inheritParams g_km
#' @inheritParams h_ggkm
#' @param g_el (`list` of `gtable`)\cr list as obtained by `h_decompose_gg()`.
#' @param annot_at_risk (`flag`)\cr compute and add the annotation table reporting the number of
#'   patient at risk matching the main grid of the Kaplan-Meier curve.
#'
#' @return A grid layout.
#'
#' @details The layout corresponds to a grid of two columns and five rows of unequal dimensions. Most of the
#'   dimension are fixed, only the curve is flexible and will accommodate with the remaining free space.
#'   * The left column gets the annotation of the `ggplot` (y-axis) and the names of the strata for the patient
#'     at risk tabulation. The main constraint is about the width of the columns which must allow the writing of
#'     the strata name.
#'   * The right column receive the `ggplot`, the legend, the x-axis and the patient at risk table.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "tt", footnotes = "ff", yval = "Survival"
#' )
#' g_el <- h_decompose_gg(gg)
#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")
#' grid.show.layout(lyt)
#' }
#'
#' @export
h_km_layout <- function(data, g_el, title, footnotes, annot_at_risk = TRUE, annot_at_risk_title = TRUE) {
  txtlines <- levels(as.factor(data$strata))
  nlines <- nlevels(as.factor(data$strata))
  col_annot_width <- max(
    c(
      as.numeric(grid::convertX(g_el$yaxis$widths + g_el$ylab$widths, "pt")),
      as.numeric(
        grid::convertX(
          grid::stringWidth(txtlines) + grid::unit(7, "pt"), "pt"
        )
      )
    )
  )

  ttl_row <- as.numeric(!is.null(title))
  foot_row <- as.numeric(!is.null(footnotes))
  no_tbl_ind <- c()
  ht_x <- c()
  ht_units <- c()

  if (ttl_row == 1) {
    no_tbl_ind <- c(no_tbl_ind, TRUE)
    ht_x <- c(ht_x, 2)
    ht_units <- c(ht_units, "lines")
  }

  no_tbl_ind <- c(no_tbl_ind, rep(TRUE, 3), rep(FALSE, 2))
  ht_x <- c(
    ht_x,
    1,
    grid::convertX(with(g_el, xaxis$heights + ylab$widths), "pt") + grid::unit(5, "pt"),
    grid::convertX(g_el$guide$heights, "pt") + grid::unit(2, "pt"),
    1,
    nlines + 0.5,
    grid::convertX(with(g_el, xaxis$heights + ylab$widths), "pt")
  )
  ht_units <- c(
    ht_units,
    "null",
    "pt",
    "pt",
    "lines",
    "lines",
    "pt"
  )

  if (foot_row == 1) {
    no_tbl_ind <- c(no_tbl_ind, TRUE)
    ht_x <- c(ht_x, 1)
    ht_units <- c(ht_units, "lines")
  }
  if (annot_at_risk) {
    no_at_risk_tbl <- rep(TRUE, 6 + ttl_row + foot_row)
    if (!annot_at_risk_title) {
      no_at_risk_tbl[length(no_at_risk_tbl) - 2 - foot_row] <- FALSE
    }
  } else {
    no_at_risk_tbl <- no_tbl_ind
  }

  grid::grid.layout(
    nrow = sum(no_at_risk_tbl), ncol = 2,
    widths = grid::unit(c(col_annot_width, 1), c("pt", "null")),
    heights = grid::unit(
      x = ht_x[no_at_risk_tbl],
      units = ht_units[no_at_risk_tbl]
    )
  )
}

#' Helper: Patient-at-Risk Grobs
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Two graphical objects are obtained, one corresponding to row labeling and the second to the table of
#' numbers of patients at risk. If `title = TRUE`, a third object corresponding to the table title is
#' also obtained.
#'
#' @inheritParams g_km
#' @inheritParams h_ggkm
#' @param annot_tbl (`data.frame`)\cr annotation as prepared by [survival::summary.survfit()] which
#'   includes the number of patients at risk at given time points.
#' @param xlim (`numeric`)\cr the maximum value on the x-axis (used to
#'   ensure the at risk table aligns with the KM graph).
#' @param title (`flag`)\cr whether the "Patients at Risk" title should be added above the `annot_at_risk`
#'   table. Has no effect if `annot_at_risk` is `FALSE`. Defaults to `TRUE`.
#'
#' @return A named `list` of two `gTree` objects if `title = FALSE`: `at_risk` and `label`, or three
#'   `gTree` objects if `title = TRUE`: `at_risk`, `label`, and `title`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#'
#' data_plot <- h_data_plot(fit_km = fit_km)
#'
#' xticks <- h_xticks(data = data_plot)
#'
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "tt", footnotes = "ff", yval = "Survival"
#' )
#'
#' # The annotation table reports the patient at risk for a given strata and
#' # times (`xticks`).
#' annot_tbl <- summary(fit_km, times = xticks)
#' if (is.null(fit_km$strata)) {
#'   annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = "All"))
#' } else {
#'   strata_lst <- strsplit(sub("=", "equals", levels(annot_tbl$strata)), "equals")
#'   levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]
#'   annot_tbl <- data.frame(
#'     n.risk = annot_tbl$n.risk,
#'     time = annot_tbl$time,
#'     strata = annot_tbl$strata
#'   )
#' }
#'
#' # The annotation table is transformed into a grob.
#' tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks))
#'
#' # For the representation, the layout is estimated for which the decomposition
#' # of the graphic element is necessary.
#' g_el <- h_decompose_gg(gg)
#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")
#'
#' grid::grid.newpage()
#' pushViewport(viewport(layout = lyt, height = .95, width = .95))
#' grid.rect(gp = grid::gpar(lty = 1, col = "purple", fill = "gray85", lwd = 1))
#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2))
#' grid.rect(gp = grid::gpar(lty = 1, col = "orange", fill = "gray85", lwd = 1))
#' grid::grid.draw(tbl$at_risk)
#' popViewport()
#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1))
#' grid.rect(gp = grid::gpar(lty = 1, col = "green3", fill = "gray85", lwd = 1))
#' grid::grid.draw(tbl$label)
#' }
#'
#' @export
h_grob_tbl_at_risk <- function(data, annot_tbl, xlim, title = TRUE) {
  txtlines <- levels(as.factor(data$strata))
  nlines <- nlevels(as.factor(data$strata))
  y_int <- annot_tbl$time[2] - annot_tbl$time[1]
  annot_tbl <- expand.grid(
    time = seq(0, xlim, y_int),
    strata = unique(annot_tbl$strata)
  ) %>% dplyr::left_join(annot_tbl, by = c("time", "strata"))
  annot_tbl[is.na(annot_tbl)] <- 0
  y_str_unit <- as.numeric(annot_tbl$strata)
  vp_table <- grid::plotViewport(margins = grid::unit(c(0, 0, 0, 0), "lines"))
  if (title) {
    gb_table_title <- grid::gList(
      grid::textGrob(
        label = "Patients at Risk:",
        x = 1,
        y = grid::unit(0.2, "native"),
        gp = grid::gpar(fontface = "bold", fontsize = 10)
      )
    )
  }
  gb_table_left_annot <- grid::gList(
    grid::rectGrob(
      x = 0, y = grid::unit(c(1:nlines) - 1, "lines"),
      gp = grid::gpar(fill = c("gray95", "gray90"), alpha = 1, col = "white"),
      height = grid::unit(1, "lines"), just = "bottom", hjust = 0
    ),
    grid::textGrob(
      label = unique(annot_tbl$strata),
      x = 0.5,
      y = grid::unit(
        (max(unique(y_str_unit)) - unique(y_str_unit)) + 0.75,
        "native"
      ),
      gp = grid::gpar(fontface = "italic", fontsize = 10)
    )
  )
  gb_patient_at_risk <- grid::gList(
    grid::rectGrob(
      x = 0, y = grid::unit(c(1:nlines) - 1, "lines"),
      gp = grid::gpar(fill = c("gray95", "gray90"), alpha = 1, col = "white"),
      height = grid::unit(1, "lines"), just = "bottom", hjust = 0
    ),
    grid::textGrob(
      label = annot_tbl$n.risk,
      x = grid::unit(annot_tbl$time, "native"),
      y = grid::unit(
        (max(y_str_unit) - y_str_unit) + .5,
        "line"
      ) # maybe native
    )
  )

  ret <- list(
    at_risk = grid::gList(
      grid::gTree(
        vp = vp_table,
        children = grid::gList(
          grid::gTree(
            vp = grid::dataViewport(
              xscale = c(0, xlim) + c(-0.05, 0.05) * xlim,
              yscale = c(0, nlines + 1),
              extension = c(0.05, 0)
            ),
            children = grid::gList(gb_patient_at_risk)
          )
        )
      )
    ),
    label = grid::gList(
      grid::gTree(
        vp = grid::viewport(width = max(grid::stringWidth(txtlines))),
        children = grid::gList(
          grid::gTree(
            vp = grid::dataViewport(
              xscale = 0:1,
              yscale = c(0, nlines + 1),
              extension = c(0.0, 0)
            ),
            children = grid::gList(gb_table_left_annot)
          )
        )
      )
    )
  )

  if (title) {
    ret[["title"]] <- grid::gList(
      grid::gTree(
        vp = grid::viewport(width = max(grid::stringWidth(txtlines))),
        children = grid::gList(
          grid::gTree(
            vp = grid::dataViewport(
              xscale = 0:1,
              yscale = c(0, 1),
              extension = c(0, 0)
            ),
            children = grid::gList(gb_table_title)
          )
        )
      )
    )
  }

  ret
}

#' Helper Function: Survival Estimations
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Transform a survival fit to a table with groups in rows characterized by N, median and confidence interval.
#'
#' @inheritParams h_data_plot
#'
#' @return A summary table with statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#'
#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "OS")
#' fit <- survfit(
#'   formula = Surv(AVAL, 1 - CNSR) ~ ARMCD,
#'   data = adtte
#' )
#' h_tbl_median_surv(fit_km = fit)
#' }
#'
#' @export
h_tbl_median_surv <- function(fit_km, armval = "All") {
  y <- if (is.null(fit_km$strata)) {
    as.data.frame(t(summary(fit_km)$table), row.names = armval)
  } else {
    tbl <- summary(fit_km)$table
    rownames_lst <- strsplit(sub("=", "equals", rownames(tbl)), "equals")
    rownames(tbl) <- matrix(unlist(rownames_lst), ncol = 2, byrow = TRUE)[, 2]
    as.data.frame(tbl)
  }
  conf.int <- summary(fit_km)$conf.int # nolint
  y$records <- round(y$records)
  y$median <- signif(y$median, 4)
  y$`CI` <- paste0(
    "(", signif(y[[paste0(conf.int, "LCL")]], 4), ", ", signif(y[[paste0(conf.int, "UCL")]], 4), ")"
  )
  stats::setNames(
    y[c("records", "median", "CI")],
    c("N", "Median", f_conf_level(conf.int))
  )
}

#' Helper Function: Survival Estimation Grob
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The survival fit is transformed in a grob containing a table with groups in
#' rows characterized by N, median and 95% confidence interval.
#'
#' @inheritParams g_km
#' @inheritParams h_data_plot
#' @param ttheme (`list`)\cr see [gridExtra::ttheme_default()].
#' @param x (`numeric`)\cr a value between 0 and 1 specifying x-location.
#' @param y (`numeric`)\cr a value between 0 and 1 specifying y-location.
#' @param width (`unit`)\cr width (as a unit) to use when printing the grob.
#'
#' @return A `grob` of a table containing statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "pink", fill = "gray85", lwd = 1))
#' tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_grob_median_surv() %>%
#'   grid::grid.draw()
#' }
#'
#' @export
h_grob_median_surv <- function(fit_km,
                               armval = "All",
                               x = 0.9,
                               y = 0.9,
                               width = grid::unit(0.3, "npc"),
                               ttheme = gridExtra::ttheme_default()) {
  data <- h_tbl_median_surv(fit_km, armval = armval)

  width <- grid::convertUnit(grid::unit(as.numeric(width), grid::unitType(width)), "in")
  height <- width * (nrow(data) + 1) / 12

  w <- paste(" ", c(
    rownames(data)[which.max(nchar(rownames(data)))],
    sapply(names(data), function(x) c(x, data[[x]])[which.max(nchar(c(x, data[[x]])))])
  ))
  w_unit <- grid::convertWidth(grid::stringWidth(w), "in", valueOnly = TRUE)

  w_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strwidth(w[4], units = "in")
  })
  f_size_w <- which.max(w_txt[w_txt < as.numeric((w_unit / sum(w_unit)) * width)[4]])

  h_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strheight(grid::stringHeight("X"), units = "in")
  })
  f_size_h <- which.max(h_txt[h_txt < as.numeric(grid::unit(as.numeric(height) / 4, grid::unitType(height)))])

  if (ttheme$core$fg_params$fontsize == 12) {
    ttheme$core$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$colhead$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$rowhead$fg_params$fontsize <- min(f_size_w, f_size_h)
  }

  gt <- gridExtra::tableGrob(
    d = data,
    theme = ttheme
  )
  gt$widths <- ((w_unit / sum(w_unit)) * width)
  gt$heights <- rep(grid::unit(as.numeric(height) / 4, grid::unitType(height)), nrow(gt))

  vp <- grid::viewport(
    x = grid::unit(x, "npc") + grid::unit(1, "lines"),
    y = grid::unit(y, "npc") + grid::unit(1.5, "lines"),
    height = height,
    width = width,
    just = c("right", "top")
  )

  grid::gList(
    grid::gTree(
      vp = vp,
      children = grid::gList(gt)
    )
  )
}

#' Helper: Grid Object with y-axis Annotation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Build the y-axis annotation from a decomposed `ggplot`.
#'
#' @param ylab (`gtable`)\cr the y-lab as a graphical object derived from a `ggplot`.
#' @param yaxis (`gtable`)\cr the y-axis as a graphical object derived from a `ggplot`.
#'
#' @return a `gTree` object containing the y-axis annotation from a `ggplot`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "title", footnotes = "footnotes", yval = "Survival"
#' )
#'
#' g_el <- h_decompose_gg(gg)
#'
#' grid::grid.newpage()
#' pvp <- grid::plotViewport(margins = c(5, 4, 2, 20))
#' pushViewport(pvp)
#' grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis))
#' grid.rect(gp = grid::gpar(lty = 1, col = "gray35", fill = NA))
#' }
#'
#' @export
h_grob_y_annot <- function(ylab, yaxis) {
  grid::gList(
    grid::gTree(
      vp = grid::viewport(
        width = grid::convertX(yaxis$widths + ylab$widths, "pt"),
        x = grid::unit(1, "npc"),
        just = "right"
      ),
      children = grid::gList(cbind(ylab, yaxis))
    )
  )
}

#' Helper Function: Pairwise `CoxPH` table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Create a `data.frame` of pairwise stratified or unstratified `CoxPH` analysis results.
#'
#' @inheritParams g_km
#'
#' @return A `data.frame` containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),
#'   and `p-value (log-rank)`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#'
#' adtte <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#'
#' h_tbl_coxph_pairwise(
#'   df = adtte,
#'   variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM"),
#'   control_coxph_pw = control_coxph(conf_level = 0.9)
#' )
#' }
#'
#' @export
h_tbl_coxph_pairwise <- function(df,
                                 variables,
                                 ref_group_coxph = NULL,
                                 control_coxph_pw = control_coxph(),
                                 annot_coxph_ref_lbls = FALSE) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_tbl_coxph_pairwise() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  assert_df_with_variables(df, variables)
  checkmate::assert_choice(ref_group_coxph, levels(df[[variables$arm]]), null.ok = TRUE)
  checkmate::assert_flag(annot_coxph_ref_lbls)

  arm <- variables$arm
  df[[arm]] <- factor(df[[arm]])

  ref_group <- if (!is.null(ref_group_coxph)) ref_group_coxph else levels(df[[variables$arm]])[1]
  comp_group <- setdiff(levels(df[[arm]]), ref_group)

  results <- Map(function(comp) {
    res <- s_coxph_pairwise(
      df = df[df[[arm]] == comp, , drop = FALSE],
      .ref_group = df[df[[arm]] == ref_group, , drop = FALSE],
      .in_ref_col = FALSE,
      .var = variables$tte,
      is_event = variables$is_event,
      strata = variables$strata,
      control = control_coxph_pw
    )
    res_df <- data.frame(
      hr = format(round(res$hr, 2), nsmall = 2),
      hr_ci = paste0(
        "(", format(round(res$hr_ci[1], 2), nsmall = 2), ", ",
        format(round(res$hr_ci[2], 2), nsmall = 2), ")"
      ),
      pvalue = if (res$pvalue < 0.0001) "<0.0001" else format(round(res$pvalue, 4), 4),
      stringsAsFactors = FALSE
    )
    colnames(res_df) <- c("HR", vapply(res[c("hr_ci", "pvalue")], obj_label, FUN.VALUE = "character"))
    row.names(res_df) <- comp
    res_df
  }, comp_group)
  if (annot_coxph_ref_lbls) names(results) <- paste(comp_group, "vs.", ref_group)

  do.call(rbind, results)
}

#' Helper Function: `CoxPH` Grob
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Grob of `rtable` output from [h_tbl_coxph_pairwise()]
#'
#' @inheritParams h_grob_median_surv
#' @param ... arguments will be passed to [h_tbl_coxph_pairwise()].
#' @param x (`numeric`)\cr a value between 0 and 1 specifying x-location.
#' @param y (`numeric`)\cr a value between 0 and 1 specifying y-location.
#' @param width (`unit`)\cr width (as a unit) to use when printing the grob.
#'
#' @return A `grob` of a table containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),
#'   and `p-value (log-rank)`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "pink", fill = "gray85", lwd = 1))
#' data <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#' tbl_grob <- h_grob_coxph(
#'   df = data,
#'   variables = list(tte = "AVAL", is_event = "is_event", arm = "ARMCD"),
#'   control_coxph_pw = control_coxph(conf_level = 0.9), x = 0.5, y = 0.5
#' )
#' grid::grid.draw(tbl_grob)
#' }
#'
#' @export
h_grob_coxph <- function(...,
                         x = 0,
                         y = 0,
                         width = grid::unit(0.4, "npc"),
                         ttheme = gridExtra::ttheme_default(
                           padding = grid::unit(c(1, .5), "lines"),
                           core = list(bg_params = list(fill = c("grey95", "grey90"), alpha = .5))
                         )) {
  data <- h_tbl_coxph_pairwise(...)

  width <- grid::convertUnit(grid::unit(as.numeric(width), grid::unitType(width)), "in")
  height <- width * (nrow(data) + 1) / 12

  w <- paste("    ", c(
    rownames(data)[which.max(nchar(rownames(data)))],
    sapply(names(data), function(x) c(x, data[[x]])[which.max(nchar(c(x, data[[x]])))])
  ))
  w_unit <- grid::convertWidth(grid::stringWidth(w), "in", valueOnly = TRUE)

  w_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strwidth(w[4], units = "in")
  })
  f_size_w <- which.max(w_txt[w_txt < as.numeric((w_unit / sum(w_unit)) * width)[4]])

  h_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strheight(grid::stringHeight("X"), units = "in")
  })
  f_size_h <- which.max(h_txt[h_txt < as.numeric(grid::unit(as.numeric(height) / 4, grid::unitType(height)))])

  if (ttheme$core$fg_params$fontsize == 12) {
    ttheme$core$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$colhead$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$rowhead$fg_params$fontsize <- min(f_size_w, f_size_h)
  }

  tryCatch(
    expr = {
      gt <- gridExtra::tableGrob(
        d = data,
        theme = ttheme
      ) # ERROR 'data' must be of a vector type, was 'NULL'
      gt$widths <- ((w_unit / sum(w_unit)) * width)
      gt$heights <- rep(grid::unit(as.numeric(height) / 4, grid::unitType(height)), nrow(gt))
      vp <- grid::viewport(
        x = grid::unit(x, "npc") + grid::unit(1, "lines"),
        y = grid::unit(y, "npc") + grid::unit(1.5, "lines"),
        height = height,
        width = width,
        just = c("left", "bottom")
      )
      grid::gList(
        grid::gTree(
          vp = vp,
          children = grid::gList(gt)
        )
      )
    },
    error = function(w) {
      message(paste(
        "Warning: Cox table will not be displayed as there is",
        "not any level to be compared in the arm variable."
      ))
      return(
        grid::gList(
          grid::gTree(
            vp = NULL,
            children = NULL
          )
        )
      )
    }
  )
}

#' Proportion Difference
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams prop_diff_strat_nc
#' @inheritParams argument_convention
#' @param method (`string`)\cr the method used for the confidence interval estimation.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_proportion_diff")`
#'   to see available statistics for this function.
#'
#' @seealso [d_proportion_diff()]
#'
#' @name prop_diff
#' @order 1
NULL

#' @describeIn prop_diff Statistics function estimating the difference
#'   in terms of responder proportion.
#'
#' @return
#' * `s_proportion_diff()` returns a named list of elements `diff` and `diff_ci`.
#'
#' @note When performing an unstratified analysis, methods `"cmh"`, `"strat_newcombe"`, and `"strat_newcombecc"` are
#'   not permitted.
#'
#' @examples
#' s_proportion_diff(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   conf_level = 0.90,
#'   method = "ha"
#' )
#'
#' # CMH example with strata
#' s_proportion_diff(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   variables = list(strata = c("f1", "f2")),
#'   conf_level = 0.90,
#'   method = "cmh"
#' )
#'
#' @export
s_proportion_diff <- function(df,
                              .var,
                              .ref_group,
                              .in_ref_col,
                              variables = list(strata = NULL),
                              conf_level = 0.95,
                              method = c(
                                "waldcc", "wald", "cmh",
                                "ha", "newcombe", "newcombecc",
                                "strat_newcombe", "strat_newcombecc"
                              ),
                              weights_method = "cmh") {
  method <- match.arg(method)
  if (is.null(variables$strata) && checkmate::test_subset(method, c("cmh", "strat_newcombe", "strat_newcombecc"))) {
    stop(paste(
      "When performing an unstratified analysis, methods 'cmh', 'strat_newcombe', and 'strat_newcombecc' are not",
      "permitted. Please choose a different method."
    ))
  }
  y <- list(diff = "", diff_ci = "")

  if (!.in_ref_col) {
    rsp <- c(.ref_group[[.var]], df[[.var]])
    grp <- factor(
      rep(
        c("ref", "Not-ref"),
        c(nrow(.ref_group), nrow(df))
      ),
      levels = c("ref", "Not-ref")
    )

    if (!is.null(variables$strata)) {
      strata_colnames <- variables$strata
      checkmate::assert_character(strata_colnames, null.ok = FALSE)
      strata_vars <- stats::setNames(as.list(strata_colnames), strata_colnames)

      assert_df_with_variables(df, strata_vars)
      assert_df_with_variables(.ref_group, strata_vars)

      # Merging interaction strata for reference group rows data and remaining
      strata <- c(
        interaction(.ref_group[strata_colnames]),
        interaction(df[strata_colnames])
      )
      strata <- as.factor(strata)
    }

    # Defining the std way to calculate weights for strat_newcombe
    if (!is.null(variables$weights_method)) {
      weights_method <- variables$weights_method
    } else {
      weights_method <- "cmh"
    }

    y <- switch(method,
      "wald" = prop_diff_wald(rsp, grp, conf_level, correct = FALSE),
      "waldcc" = prop_diff_wald(rsp, grp, conf_level, correct = TRUE),
      "ha" = prop_diff_ha(rsp, grp, conf_level),
      "newcombe" = prop_diff_nc(rsp, grp, conf_level, correct = FALSE),
      "newcombecc" = prop_diff_nc(rsp, grp, conf_level, correct = TRUE),
      "strat_newcombe" = prop_diff_strat_nc(rsp,
        grp,
        strata,
        weights_method,
        conf_level,
        correct = FALSE
      ),
      "strat_newcombecc" = prop_diff_strat_nc(rsp,
        grp,
        strata,
        weights_method,
        conf_level,
        correct = TRUE
      ),
      "cmh" = prop_diff_cmh(rsp, grp, strata, conf_level)[c("diff", "diff_ci")]
    )

    y$diff <- y$diff * 100
    y$diff_ci <- y$diff_ci * 100
  }

  attr(y$diff, "label") <- "Difference in Response rate (%)"
  attr(y$diff_ci, "label") <- d_proportion_diff(
    conf_level, method,
    long = FALSE
  )

  y
}

#' @describeIn prop_diff Formatted analysis function which is used as `afun` in `estimate_proportion_diff()`.
#'
#' @return
#' * `a_proportion_diff()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_proportion_diff(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   conf_level = 0.90,
#'   method = "ha"
#' )
#'
#' @export
a_proportion_diff <- make_afun(
  s_proportion_diff,
  .formats = c(diff = "xx.x", diff_ci = "(xx.x, xx.x)"),
  .indent_mods = c(diff = 0L, diff_ci = 1L)
)

#' @describeIn prop_diff Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `estimate_proportion_diff()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_proportion_diff()` to the table layout.
#'
#' @examples
#' ## "Mid" case: 4/4 respond in group A, 1/2 respond in group B.
#' nex <- 100 # Number of example rows
#' dta <- data.frame(
#'   "rsp" = sample(c(TRUE, FALSE), nex, TRUE),
#'   "grp" = sample(c("A", "B"), nex, TRUE),
#'   "f1" = sample(c("a1", "a2"), nex, TRUE),
#'   "f2" = sample(c("x", "y", "z"), nex, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' l <- basic_table() %>%
#'   split_cols_by(var = "grp", ref_group = "B") %>%
#'   estimate_proportion_diff(
#'     vars = "rsp",
#'     conf_level = 0.90,
#'     method = "ha"
#'   )
#'
#' build_table(l, df = dta)
#'
#' @export
#' @order 2
estimate_proportion_diff <- function(lyt,
                                     vars,
                                     variables = list(strata = NULL),
                                     conf_level = 0.95,
                                     method = c(
                                       "waldcc", "wald", "cmh",
                                       "ha", "newcombe", "newcombecc",
                                       "strat_newcombe", "strat_newcombecc"
                                     ),
                                     weights_method = "cmh",
                                     na_str = default_na_str(),
                                     nested = TRUE,
                                     ...,
                                     var_labels = vars,
                                     show_labels = "hidden",
                                     table_names = vars,
                                     .stats = NULL,
                                     .formats = NULL,
                                     .labels = NULL,
                                     .indent_mods = NULL) {
  extra_args <- list(
    variables = variables, conf_level = conf_level, method = method, weights_method = weights_method, ...
  )

  afun <- make_afun(
    a_proportion_diff,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    afun = afun,
    var_labels = var_labels,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Check: Proportion Difference Arguments
#'
#' Verifies that and/or convert arguments into valid values to be used in the
#' estimation of difference in responder proportions.
#'
#' @inheritParams prop_diff
#' @inheritParams prop_diff_wald
#'
#' @keywords internal
check_diff_prop_ci <- function(rsp,
                               grp,
                               strata = NULL,
                               conf_level,
                               correct = NULL) {
  checkmate::assert_logical(rsp, any.missing = FALSE)
  checkmate::assert_factor(grp, len = length(rsp), any.missing = FALSE, n.levels = 2)
  checkmate::assert_number(conf_level, lower = 0, upper = 1)
  checkmate::assert_flag(correct, null.ok = TRUE)

  if (!is.null(strata)) {
    checkmate::assert_factor(strata, len = length(rsp))
  }

  invisible()
}

#' Description of Method Used for Proportion Comparison
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function that describes the analysis in
#' `s_proportion_diff`.
#'
#' @inheritParams s_proportion_diff
#' @param long (`logical`)\cr Whether a long or a short (default) description is required.
#'
#' @return A `string` describing the analysis.
#'
#' @seealso [prop_diff]
#'
#' @export
d_proportion_diff <- function(conf_level,
                              method,
                              long = FALSE) {
  label <- paste0(conf_level * 100, "% CI")
  if (long) {
    label <- paste(
      label,
      ifelse(
        method == "cmh",
        "for adjusted difference",
        "for difference"
      )
    )
  }

  method_part <- switch(method,
    "cmh" = "CMH, without correction",
    "waldcc" = "Wald, with correction",
    "wald" = "Wald, without correction",
    "ha" = "Anderson-Hauck",
    "newcombe" = "Newcombe, without correction",
    "newcombecc" = "Newcombe, with correction",
    "strat_newcombe" = "Stratified Newcombe, without correction",
    "strat_newcombecc" = "Stratified Newcombe, with correction",
    stop(paste(method, "does not have a description"))
  )
  paste0(label, " (", method_part, ")")
}

#' Helper Functions to Calculate Proportion Difference
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @inheritParams prop_diff
#' @param grp (`factor`)\cr vector assigning observations to one out of two groups
#'   (e.g. reference and treatment group).
#'
#' @return A named `list` of elements `diff` (proportion difference) and `diff_ci`
#'   (proportion difference confidence interval).
#'
#' @seealso [prop_diff()] for implementation of these helper functions.
#'
#' @name h_prop_diff
NULL

#' @describeIn h_prop_diff The Wald interval follows the usual textbook
#'   definition for a single proportion confidence interval using the normal
#'   approximation. It is possible to include a continuity correction for Wald's
#'   interval.
#'
#' @param correct (`logical`)\cr whether to include the continuity correction. For further
#'   information, see [stats::prop.test()].
#'
#' @examples
#' # Wald confidence interval
#' set.seed(2)
#' rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20)
#' grp <- factor(c(rep("A", 10), rep("B", 10)))
#'
#' prop_diff_wald(rsp = rsp, grp = grp, conf_level = 0.95, correct = FALSE)
#'
#' @export
prop_diff_wald <- function(rsp,
                           grp,
                           conf_level = 0.95,
                           correct = FALSE) {
  if (isTRUE(correct)) {
    mthd <- "waldcc"
  } else {
    mthd <- "wald"
  }
  grp <- as_factor_keep_attributes(grp)
  check_diff_prop_ci(
    rsp = rsp, grp = grp, conf_level = conf_level, correct = correct
  )

  # check if binary response is coded as logical
  checkmate::assert_logical(rsp, any.missing = FALSE)
  checkmate::assert_factor(grp, len = length(rsp), any.missing = FALSE, n.levels = 2)

  tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
  # x1 and n1 are non-reference groups.
  diff_ci <- desctools_binom(
    x1 = tbl[2], n1 = sum(tbl[2], tbl[4]),
    x2 = tbl[1], n2 = sum(tbl[1], tbl[3]),
    conf.level = conf_level,
    method = mthd
  )

  list(
    "diff" = unname(diff_ci[, "est"]),
    "diff_ci" = unname(diff_ci[, c("lwr.ci", "upr.ci")])
  )
}

#' @describeIn h_prop_diff Anderson-Hauck confidence interval.
#'
#' @examples
#' # Anderson-Hauck confidence interval
#' ## "Mid" case: 3/4 respond in group A, 1/2 respond in group B.
#' rsp <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE)
#' grp <- factor(c("A", "B", "A", "B", "A", "A"), levels = c("B", "A"))
#'
#' prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.90)
#'
#' ## Edge case: Same proportion of response in A and B.
#' rsp <- c(TRUE, FALSE, TRUE, FALSE)
#' grp <- factor(c("A", "A", "B", "B"), levels = c("A", "B"))
#'
#' prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.6)
#'
#' @export
prop_diff_ha <- function(rsp,
                         grp,
                         conf_level) {
  grp <- as_factor_keep_attributes(grp)
  check_diff_prop_ci(rsp = rsp, grp = grp, conf_level = conf_level)

  tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
  # x1 and n1 are non-reference groups.
  ci <- desctools_binom(
    x1 = tbl[2], n1 = sum(tbl[2], tbl[4]),
    x2 = tbl[1], n2 = sum(tbl[1], tbl[3]),
    conf.level = conf_level,
    method = "ha"
  )
  list(
    "diff" = unname(ci[, "est"]),
    "diff_ci" = unname(ci[, c("lwr.ci", "upr.ci")])
  )
}

#' @describeIn h_prop_diff `Newcombe` confidence interval. It is based on
#'   the Wilson score confidence interval for a single binomial proportion.
#'
#' @examples
#' # `Newcombe` confidence interval
#'
#' set.seed(1)
#' rsp <- c(
#'   sample(c(TRUE, FALSE), size = 40, prob = c(3 / 4, 1 / 4), replace = TRUE),
#'   sample(c(TRUE, FALSE), size = 40, prob = c(1 / 2, 1 / 2), replace = TRUE)
#' )
#' grp <- factor(rep(c("A", "B"), each = 40), levels = c("B", "A"))
#' table(rsp, grp)
#'
#' prop_diff_nc(rsp = rsp, grp = grp, conf_level = 0.9)
#'
#' @export
prop_diff_nc <- function(rsp,
                         grp,
                         conf_level,
                         correct = FALSE) {
  if (isTRUE(correct)) {
    mthd <- "scorecc"
  } else {
    mthd <- "score"
  }
  grp <- as_factor_keep_attributes(grp)
  check_diff_prop_ci(rsp = rsp, grp = grp, conf_level = conf_level)

  p_grp <- tapply(rsp, grp, mean)
  diff_p <- unname(diff(p_grp))
  tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
  ci <- desctools_binom(
    # x1 and n1 are non-reference groups.
    x1 = tbl[2], n1 = sum(tbl[2], tbl[4]),
    x2 = tbl[1], n2 = sum(tbl[1], tbl[3]),
    conf.level = conf_level,
    method = mthd
  )
  list(
    "diff" = unname(ci[, "est"]),
    "diff_ci" = unname(ci[, c("lwr.ci", "upr.ci")])
  )
}

#' @describeIn h_prop_diff Calculates the weighted difference. This is defined as the difference in
#'   response rates between the experimental treatment group and the control treatment group, adjusted
#'   for stratification factors by applying `Cochran-Mantel-Haenszel` (`CMH`) weights. For the `CMH` chi-squared
#'   test, use [stats::mantelhaen.test()].
#'
#' @param strata (`factor`)\cr variable with one level per stratum and same length as `rsp`.
#'
#' @examples
#' # Cochran-Mantel-Haenszel confidence interval
#'
#' set.seed(2)
#' rsp <- sample(c(TRUE, FALSE), 100, TRUE)
#' grp <- sample(c("Placebo", "Treatment"), 100, TRUE)
#' grp <- factor(grp, levels = c("Placebo", "Treatment"))
#' strata_data <- data.frame(
#'   "f1" = sample(c("a", "b"), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' prop_diff_cmh(
#'   rsp = rsp, grp = grp, strata = interaction(strata_data),
#'   conf_level = 0.90
#' )
#'
#' @export
prop_diff_cmh <- function(rsp,
                          grp,
                          strata,
                          conf_level = 0.95) {
  grp <- as_factor_keep_attributes(grp)
  strata <- as_factor_keep_attributes(strata)
  check_diff_prop_ci(
    rsp = rsp, grp = grp, conf_level = conf_level, strata = strata
  )

  if (any(tapply(rsp, strata, length) < 5)) {
    warning("Less than 5 observations in some strata.")
  }

  # first dimension: FALSE, TRUE
  # 2nd dimension: CONTROL, TX
  # 3rd dimension: levels of strata
  # rsp as factor rsp to handle edge case of no FALSE (or TRUE) rsp records
  t_tbl <- table(
    factor(rsp, levels = c("FALSE", "TRUE")),
    grp,
    strata
  )
  n1 <- colSums(t_tbl[1:2, 1, ])
  n2 <- colSums(t_tbl[1:2, 2, ])
  p1 <- t_tbl[2, 1, ] / n1
  p2 <- t_tbl[2, 2, ] / n2
  # CMH weights
  use_stratum <- (n1 > 0) & (n2 > 0)
  n1 <- n1[use_stratum]
  n2 <- n2[use_stratum]
  p1 <- p1[use_stratum]
  p2 <- p2[use_stratum]
  wt <- (n1 * n2 / (n1 + n2))
  wt_normalized <- wt / sum(wt)
  est1 <- sum(wt_normalized * p1)
  est2 <- sum(wt_normalized * p2)
  estimate <- c(est1, est2)
  names(estimate) <- levels(grp)
  se1 <- sqrt(sum(wt_normalized^2 * p1 * (1 - p1) / n1))
  se2 <- sqrt(sum(wt_normalized^2 * p2 * (1 - p2) / n2))
  z <- stats::qnorm((1 + conf_level) / 2)
  err1 <- z * se1
  err2 <- z * se2
  ci1 <- c((est1 - err1), (est1 + err1))
  ci2 <- c((est2 - err2), (est2 + err2))
  estimate_ci <- list(ci1, ci2)
  names(estimate_ci) <- levels(grp)
  diff_est <- est2 - est1
  se_diff <- sqrt(sum(((p1 * (1 - p1) / n1) + (p2 * (1 - p2) / n2)) * wt_normalized^2))
  diff_ci <- c(diff_est - z * se_diff, diff_est + z * se_diff)

  list(
    prop = estimate,
    prop_ci = estimate_ci,
    diff = diff_est,
    diff_ci = diff_ci,
    weights = wt_normalized,
    n1 = n1,
    n2 = n2
  )
}

#' @describeIn h_prop_diff Calculates the stratified `Newcombe` confidence interval and difference in response
#'   rates between the experimental treatment group and the control treatment group, adjusted for stratification
#'   factors. This implementation follows closely the one proposed by \insertCite{Yan2010-jt;textual}{tern}.
#'   Weights can be estimated from the heuristic proposed in [prop_strat_wilson()] or from `CMH`-derived weights
#'   (see [prop_diff_cmh()]).
#'
#' @param strata (`factor`)\cr variable with one level per stratum and same length as `rsp`.
#' @param weights_method (`string`)\cr weights method. Can be either `"cmh"` or `"heuristic"`
#'   and directs the way weights are estimated.
#'
#' @references
#' \insertRef{Yan2010-jt}{tern}
#'
#' @examples
#' # Stratified `Newcombe` confidence interval
#'
#' set.seed(2)
#' data_set <- data.frame(
#'   "rsp" = sample(c(TRUE, FALSE), 100, TRUE),
#'   "f1" = sample(c("a", "b"), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   "grp" = sample(c("Placebo", "Treatment"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' prop_diff_strat_nc(
#'   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),
#'   weights_method = "cmh",
#'   conf_level = 0.90
#' )
#'
#' prop_diff_strat_nc(
#'   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),
#'   weights_method = "wilson_h",
#'   conf_level = 0.90
#' )
#'
#' @export
prop_diff_strat_nc <- function(rsp,
                               grp,
                               strata,
                               weights_method = c("cmh", "wilson_h"),
                               conf_level = 0.95,
                               correct = FALSE) {
  weights_method <- match.arg(weights_method)
  grp <- as_factor_keep_attributes(grp)
  strata <- as_factor_keep_attributes(strata)
  check_diff_prop_ci(
    rsp = rsp, grp = grp, conf_level = conf_level, strata = strata
  )
  checkmate::assert_number(conf_level, lower = 0, upper = 1)
  checkmate::assert_flag(correct)
  if (any(tapply(rsp, strata, length) < 5)) {
    warning("Less than 5 observations in some strata.")
  }

  rsp_by_grp <- split(rsp, f = grp)
  strata_by_grp <- split(strata, f = grp)

  # Finding the weights
  weights <- if (identical(weights_method, "cmh")) {
    prop_diff_cmh(rsp = rsp, grp = grp, strata = strata)$weights
  } else if (identical(weights_method, "wilson_h")) {
    prop_strat_wilson(rsp, strata, conf_level = conf_level, correct = correct)$weights
  }
  weights[levels(strata)[!levels(strata) %in% names(weights)]] <- 0

  # Calculating lower (`l`) and upper (`u`) confidence bounds per group.
  strat_wilson_by_grp <- Map(
    prop_strat_wilson,
    rsp = rsp_by_grp,
    strata = strata_by_grp,
    weights = list(weights, weights),
    conf_level = conf_level,
    correct = correct
  )

  ci_ref <- strat_wilson_by_grp[[1]]
  ci_trt <- strat_wilson_by_grp[[2]]
  l_ref <- as.numeric(ci_ref$conf_int[1])
  u_ref <- as.numeric(ci_ref$conf_int[2])
  l_trt <- as.numeric(ci_trt$conf_int[1])
  u_trt <- as.numeric(ci_trt$conf_int[2])

  # Estimating the diff and n_ref, n_trt (it allows different weights to be used)
  t_tbl <- table(
    factor(rsp, levels = c("FALSE", "TRUE")),
    grp,
    strata
  )
  n_ref <- colSums(t_tbl[1:2, 1, ])
  n_trt <- colSums(t_tbl[1:2, 2, ])
  use_stratum <- (n_ref > 0) & (n_trt > 0)
  n_ref <- n_ref[use_stratum]
  n_trt <- n_trt[use_stratum]
  p_ref <- t_tbl[2, 1, use_stratum] / n_ref
  p_trt <- t_tbl[2, 2, use_stratum] / n_trt
  est1 <- sum(weights * p_ref)
  est2 <- sum(weights * p_trt)
  diff_est <- est2 - est1

  lambda1 <- sum(weights^2 / n_ref)
  lambda2 <- sum(weights^2 / n_trt)
  z <- stats::qnorm((1 + conf_level) / 2)

  lower <- diff_est - z * sqrt(lambda2 * l_trt * (1 - l_trt) + lambda1 * u_ref * (1 - u_ref))
  upper <- diff_est + z * sqrt(lambda1 * l_ref * (1 - l_ref) + lambda2 * u_trt * (1 - u_trt))

  list(
    "diff" = diff_est,
    "diff_ci" = c("lower" = lower, "upper" = upper)
  )
}

#' Confidence Intervals for a Difference of Binomials
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Several confidence intervals for the difference between proportions.
#'
#' @name desctools_binom
NULL

#' Recycle List of Parameters
#'
#' This function recycles all supplied elements to the maximal dimension.
#'
#' @param ... (`any`)\cr Elements to recycle.
#'
#' @return A `list`.
#'
#' @keywords internal
#' @noRd
h_recycle <- function(...) {
  lst <- list(...)
  maxdim <- max(lengths(lst))
  res <- lapply(lst, rep, length.out = maxdim)
  attr(res, "maxdim") <- maxdim
  return(res)
}

#' @describeIn desctools_binom Several confidence intervals for the difference between proportions.
#'
#' @return A `matrix` of 3 values:
#'   * `est`: estimate of proportion difference.
#'   * `lwr.ci`: estimate of lower end of the confidence interval.
#'   * `upr.ci`: estimate of upper end of the confidence interval.
#'
#' @keywords internal
desctools_binom <- function(x1,
                            n1,
                            x2,
                            n2,
                            conf.level = 0.95, # nolint
                            sides = c("two.sided", "left", "right"),
                            method = c(
                              "ac", "wald", "waldcc", "score", "scorecc", "mn", "mee", "blj", "ha", "hal", "jp"
                            )) {
  if (missing(sides)) {
    sides <- match.arg(sides)
  }
  if (missing(method)) {
    method <- match.arg(method)
  }
  iBinomDiffCI <- function(x1, n1, x2, n2, conf.level, sides, method) { # nolint
    if (sides != "two.sided") {
      conf.level <- 1 - 2 * (1 - conf.level) # nolint
    }
    alpha <- 1 - conf.level
    kappa <- stats::qnorm(1 - alpha / 2)
    p1_hat <- x1 / n1
    p2_hat <- x2 / n2
    est <- p1_hat - p2_hat
    switch(method,
      wald = {
        vd <- p1_hat * (1 - p1_hat) / n1 + p2_hat * (1 - p2_hat) / n2
        term2 <- kappa * sqrt(vd)
        ci_lwr <- max(-1, est - term2)
        ci_upr <- min(1, est + term2)
      },
      waldcc = {
        vd <- p1_hat * (1 - p1_hat) / n1 + p2_hat * (1 - p2_hat) / n2
        term2 <- kappa * sqrt(vd)
        term2 <- term2 + 0.5 * (1 / n1 + 1 / n2)
        ci_lwr <- max(-1, est - term2)
        ci_upr <- min(1, est + term2)
      },
      ac = {
        n1 <- n1 + 2
        n2 <- n2 + 2
        x1 <- x1 + 1
        x2 <- x2 + 1
        p1_hat <- x1 / n1
        p2_hat <- x2 / n2
        est1 <- p1_hat - p2_hat
        vd <- p1_hat * (1 - p1_hat) / n1 + p2_hat * (1 - p2_hat) / n2
        term2 <- kappa * sqrt(vd)
        ci_lwr <- max(-1, est1 - term2)
        ci_upr <- min(1, est1 + term2)
      },
      exact = {
        ci_lwr <- NA
        ci_upr <- NA
      },
      score = {
        w1 <- desctools_binomci(
          x = x1, n = n1, conf.level = conf.level,
          method = "wilson"
        )
        w2 <- desctools_binomci(
          x = x2, n = n2, conf.level = conf.level,
          method = "wilson"
        )
        l1 <- w1[2]
        u1 <- w1[3]
        l2 <- w2[2]
        u2 <- w2[3]
        ci_lwr <- est - kappa * sqrt(l1 * (1 - l1) / n1 + u2 * (1 - u2) / n2)
        ci_upr <- est + kappa * sqrt(u1 * (1 - u1) / n1 + l2 * (1 - l2) / n2)
      },
      scorecc = {
        w1 <- desctools_binomci(
          x = x1, n = n1, conf.level = conf.level,
          method = "wilsoncc"
        )
        w2 <- desctools_binomci(
          x = x2, n = n2, conf.level = conf.level,
          method = "wilsoncc"
        )
        l1 <- w1[2]
        u1 <- w1[3]
        l2 <- w2[2]
        u2 <- w2[3]
        ci_lwr <- max(-1, est - sqrt((p1_hat - l1)^2 + (u2 - p2_hat)^2))
        ci_upr <- min(1, est + sqrt((u1 - p1_hat)^2 + (p2_hat - l2)^2))
      },
      mee = {
        .score <- function(p1, n1, p2, n2, dif) {
          if (dif > 1) dif <- 1
          if (dif < -1) dif <- -1
          diff <- p1 - p2 - dif
          if (abs(diff) == 0) {
            res <- 0
          } else {
            t <- n2 / n1
            a <- 1 + t
            b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
            c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
            d <- -p1 * dif * (1 + dif)
            v <- (b / a / 3)^3 - b * c / (6 * a * a) + d / a / 2
            if (abs(v) < .Machine$double.eps) v <- 0
            s <- sqrt((b / a / 3)^2 - c / a / 3)
            u <- ifelse(v > 0, 1, -1) * s
            w <- (3.141592654 + acos(v / u^3)) / 3
            p1d <- 2 * u * cos(w) - b / a / 3
            p2d <- p1d - dif
            n <- n1 + n2
            res <- (p1d * (1 - p1d) / n1 + p2d * (1 - p2d) / n2)
          }
          return(sqrt(res))
        }
        pval <- function(delta) {
          z <- (est - delta) / .score(p1_hat, n1, p2_hat, n2, delta)
          2 * min(stats::pnorm(z), 1 - stats::pnorm(z))
        }
        ci_lwr <- max(-1, stats::uniroot(function(delta) {
          pval(delta) - alpha
        }, interval = c(-1 + 1e-06, est - 1e-06))$root)
        ci_upr <- min(1, stats::uniroot(function(delta) {
          pval(delta) - alpha
        }, interval = c(est + 1e-06, 1 - 1e-06))$root)
      },
      blj = {
        p1_dash <- (x1 + 0.5) / (n1 + 1)
        p2_dash <- (x2 + 0.5) / (n2 + 1)
        vd <- p1_dash * (1 - p1_dash) / n1 + p2_dash * (1 - p2_dash) / n2
        term2 <- kappa * sqrt(vd)
        est_dash <- p1_dash - p2_dash
        ci_lwr <- max(-1, est_dash - term2)
        ci_upr <- min(1, est_dash + term2)
      },
      ha = {
        term2 <- 1 /
          (2 * min(n1, n2)) + kappa * sqrt(p1_hat * (1 - p1_hat) / (n1 - 1) + p2_hat * (1 - p2_hat) / (n2 - 1))
        ci_lwr <- max(-1, est - term2)
        ci_upr <- min(1, est + term2)
      },
      mn = {
        .conf <- function(x1, n1, x2, n2, z, lower = FALSE) {
          p1 <- x1 / n1
          p2 <- x2 / n2
          p_hat <- p1 - p2
          dp <- 1 + ifelse(lower, 1, -1) * p_hat
          i <- 1
          while (i <= 50) {
            dp <- 0.5 * dp
            y <- p_hat + ifelse(lower, -1, 1) * dp
            score <- .score(p1, n1, p2, n2, y)
            if (score < z) {
              p_hat <- y
            }
            if ((dp < 1e-07) || (abs(z - score) < 1e-06)) {
              (break)()
            } else {
              i <- i + 1
            }
          }
          return(y)
        }
        .score <- function(p1, n1, p2, n2, dif) {
          diff <- p1 - p2 - dif
          if (abs(diff) == 0) {
            res <- 0
          } else {
            t <- n2 / n1
            a <- 1 + t
            b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
            c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
            d <- -p1 * dif * (1 + dif)
            v <- (b / a / 3)^3 - b * c / (6 * a * a) + d / a / 2
            s <- sqrt((b / a / 3)^2 - c / a / 3)
            u <- ifelse(v > 0, 1, -1) * s
            w <- (3.141592654 + acos(v / u^3)) / 3
            p1d <- 2 * u * cos(w) - b / a / 3
            p2d <- p1d - dif
            n <- n1 + n2
            var <- (p1d * (1 - p1d) / n1 + p2d * (1 - p2d) / n2) * n / (n - 1)
            res <- diff^2 / var
          }
          return(res)
        }
        z <- stats::qchisq(conf.level, 1)
        ci_lwr <- max(-1, .conf(x1, n1, x2, n2, z, TRUE))
        ci_upr <- min(1, .conf(x1, n1, x2, n2, z, FALSE))
      },
      beal = {
        a <- p1_hat + p2_hat
        b <- p1_hat - p2_hat
        u <- ((1 / n1) + (1 / n2)) / 4
        v <- ((1 / n1) - (1 / n2)) / 4
        V <- u * ((2 - a) * a - b^2) + 2 * v * (1 - a) * b # nolint
        z <- stats::qchisq(p = 1 - alpha / 2, df = 1)
        A <- sqrt(z * (V + z * u^2 * (2 - a) * a + z * v^2 * (1 - a)^2)) # nolint
        B <- (b + z * v * (1 - a)) / (1 + z * u) # nolint
        ci_lwr <- max(-1, B - A / (1 + z * u))
        ci_upr <- min(1, B + A / (1 + z * u))
      },
      hal = {
        psi <- (p1_hat + p2_hat) / 2
        u <- (1 / n1 + 1 / n2) / 4
        v <- (1 / n1 - 1 / n2) / 4
        z <- kappa
        theta <- ((p1_hat - p2_hat) + z^2 * v * (1 - 2 * psi)) / (1 + z^2 * u)
        w <- z / (1 + z^2 * u) * sqrt(u * (4 * psi * (1 - psi) - (p1_hat - p2_hat)^2) + 2 * v * (1 - 2 * psi) *
          (p1_hat - p2_hat) + 4 * z^2 * u^2 * (1 - psi) * psi + z^2 * v^2 * (1 - 2 * psi)^2) # nolint
        c(theta + w, theta - w)
        ci_lwr <- max(-1, theta - w)
        ci_upr <- min(1, theta + w)
      },
      jp = {
        psi <- 0.5 * ((x1 + 0.5) / (n1 + 1) + (x2 + 0.5) / (n2 + 1))
        u <- (1 / n1 + 1 / n2) / 4
        v <- (1 / n1 - 1 / n2) / 4
        z <- kappa
        theta <- ((p1_hat - p2_hat) + z^2 * v * (1 - 2 * psi)) / (1 + z^2 * u)
        w <- z / (1 + z^2 * u) * sqrt(u * (4 * psi * (1 - psi) - (p1_hat - p2_hat)^2) + 2 * v * (1 - 2 * psi) *
          (p1_hat - p2_hat) + 4 * z^2 * u^2 * (1 - psi) * psi + z^2 * v^2 * (1 - 2 * psi)^2) # nolint
        c(theta + w, theta - w)
        ci_lwr <- max(-1, theta - w)
        ci_upr <- min(1, theta + w)
      },
    )
    ci <- c(
      est = est, lwr.ci = min(ci_lwr, ci_upr),
      upr.ci = max(ci_lwr, ci_upr)
    )
    if (sides == "left") {
      ci[3] <- 1
    } else if (sides == "right") {
      ci[2] <- -1
    }
    return(ci)
  }
  method <- match.arg(arg = method, several.ok = TRUE)
  sides <- match.arg(arg = sides, several.ok = TRUE)
  lst <- h_recycle(
    x1 = x1, n1 = n1, x2 = x2, n2 = n2, conf.level = conf.level,
    sides = sides, method = method
  )
  res <- t(sapply(1:attr(lst, "maxdim"), function(i) {
    iBinomDiffCI(
      x1 = lst$x1[i],
      n1 = lst$n1[i], x2 = lst$x2[i], n2 = lst$n2[i], conf.level = lst$conf.level[i],
      sides = lst$sides[i], method = lst$method[i]
    )
  }))
  lgn <- h_recycle(x1 = if (is.null(names(x1))) {
    paste("x1", seq_along(x1), sep = ".")
  } else {
    names(x1)
  }, n1 = if (is.null(names(n1))) {
    paste("n1", seq_along(n1), sep = ".")
  } else {
    names(n1)
  }, x2 = if (is.null(names(x2))) {
    paste("x2", seq_along(x2), sep = ".")
  } else {
    names(x2)
  }, n2 = if (is.null(names(n2))) {
    paste("n2", seq_along(n2), sep = ".")
  } else {
    names(n2)
  }, conf.level = conf.level, sides = sides, method = method)
  xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) {
    length(unique(x)) !=
      1
  })]), 1, paste, collapse = ":")
  rownames(res) <- xn
  return(res)
}

#' @describeIn desctools_binom Compute confidence intervals for binomial proportions.
#'
#' @param x (`count`)\cr number of successes
#' @param n (`count`)\cr number of trials
#' @param conf.level (`proportion`)\cr confidence level, defaults to 0.95.
#' @param sides (`character`)\cr side of the confidence interval to compute. Must be one of `"two-sided"` (default),
#'   `"left"`, or `"right"`.
#' @param method (`character`)\cr method to use. Can be one out of: `"wald"`, `"wilson"`, `"wilsoncc"`,
#' `"agresti-coull"`, `"jeffreys"`, `"modified wilson"`, `"modified jeffreys"`, `"clopper-pearson"`, `"arcsine"`,
#' `"logit"`, `"witting"`, `"pratt"`, `"midp"`, `"lik"`, and `"blaker"`.
#'
#' @return A `matrix` with 3 columns containing:
#'   * `est`: estimate of proportion difference.
#'   * `lwr.ci`: lower end of the confidence interval.
#'   * `upr.ci`: upper end of the confidence interval.
#'
#' @keywords internal
desctools_binomci <- function(x,
                              n,
                              conf.level = 0.95, # nolint
                              sides = c("two.sided", "left", "right"),
                              method = c(
                                "wilson", "wald", "waldcc", "agresti-coull",
                                "jeffreys", "modified wilson", "wilsoncc", "modified jeffreys",
                                "clopper-pearson", "arcsine", "logit", "witting", "pratt",
                                "midp", "lik", "blaker"
                              ),
                              rand = 123,
                              tol = 1e-05) {
  if (missing(method)) {
    method <- "wilson"
  }
  if (missing(sides)) {
    sides <- "two.sided"
  }
  iBinomCI <- function(x, n, conf.level = 0.95, sides = c("two.sided", "left", "right"), # nolint
                       method = c(
                         "wilson", "wilsoncc", "wald",
                         "waldcc", "agresti-coull", "jeffreys", "modified wilson",
                         "modified jeffreys", "clopper-pearson", "arcsine", "logit",
                         "witting", "pratt", "midp", "lik", "blaker"
                       ),
                       rand = 123,
                       tol = 1e-05) {
    if (length(x) != 1) {
      stop("'x' has to be of length 1 (number of successes)")
    }
    if (length(n) != 1) {
      stop("'n' has to be of length 1 (number of trials)")
    }
    if (length(conf.level) != 1) {
      stop("'conf.level' has to be of length 1 (confidence level)")
    }
    if (conf.level < 0.5 || conf.level > 1) {
      stop("'conf.level' has to be in [0.5, 1]")
    }
    sides <- match.arg(sides, choices = c(
      "two.sided", "left",
      "right"
    ), several.ok = FALSE)
    if (sides != "two.sided") {
      conf.level <- 1 - 2 * (1 - conf.level) # nolint
    }
    alpha <- 1 - conf.level
    kappa <- stats::qnorm(1 - alpha / 2)
    p_hat <- x / n
    q_hat <- 1 - p_hat
    est <- p_hat
    switch(match.arg(arg = method, choices = c(
      "wilson",
      "wald", "waldcc", "wilsoncc", "agresti-coull", "jeffreys",
      "modified wilson", "modified jeffreys", "clopper-pearson",
      "arcsine", "logit", "witting", "pratt", "midp", "lik",
      "blaker"
    )),
    wald = {
      term2 <- kappa * sqrt(p_hat * q_hat) / sqrt(n)
      ci_lwr <- max(0, p_hat - term2)
      ci_upr <- min(1, p_hat + term2)
    },
    waldcc = {
      term2 <- kappa * sqrt(p_hat * q_hat) / sqrt(n)
      term2 <- term2 + 1 / (2 * n)
      ci_lwr <- max(0, p_hat - term2)
      ci_upr <- min(1, p_hat + term2)
    },
    wilson = {
      term1 <- (x + kappa^2 / 2) / (n + kappa^2)
      term2 <- kappa * sqrt(n) / (n + kappa^2) * sqrt(p_hat * q_hat + kappa^2 / (4 * n))
      ci_lwr <- max(0, term1 - term2)
      ci_upr <- min(1, term1 + term2)
    },
    wilsoncc = {
      lci <- (
        2 * x + kappa^2 - 1 - kappa * sqrt(kappa^2 - 2 - 1 / n + 4 * p_hat * (n * q_hat + 1))
      ) / (2 * (n + kappa^2))
      uci <- (
        2 * x + kappa^2 + 1 + kappa * sqrt(kappa^2 + 2 - 1 / n + 4 * p_hat * (n * q_hat - 1))
      ) / (2 * (n + kappa^2))
      ci_lwr <- max(0, ifelse(p_hat == 0, 0, lci))
      ci_upr <- min(1, ifelse(p_hat == 1, 1, uci))
    },
    `agresti-coull` = {
      x_tilde <- x + kappa^2 / 2
      n_tilde <- n + kappa^2
      p_tilde <- x_tilde / n_tilde
      q_tilde <- 1 - p_tilde
      est <- p_tilde
      term2 <- kappa * sqrt(p_tilde * q_tilde) / sqrt(n_tilde)
      ci_lwr <- max(0, p_tilde - term2)
      ci_upr <- min(1, p_tilde + term2)
    },
    jeffreys = {
      if (x == 0) {
        ci_lwr <- 0
      } else {
        ci_lwr <- stats::qbeta(
          alpha / 2,
          x + 0.5, n - x + 0.5
        )
      }
      if (x == n) {
        ci_upr <- 1
      } else {
        ci_upr <- stats::qbeta(1 - alpha / 2, x + 0.5, n - x + 0.5)
      }
    },
    `modified wilson` = {
      term1 <- (x + kappa^2 / 2) / (n + kappa^2)
      term2 <- kappa * sqrt(n) / (n + kappa^2) * sqrt(p_hat * q_hat + kappa^2 / (4 * n))
      if ((n <= 50 & x %in% c(1, 2)) | (n >= 51 & x %in% c(1:3))) {
        ci_lwr <- 0.5 * stats::qchisq(alpha, 2 * x) / n
      } else {
        ci_lwr <- max(0, term1 - term2)
      }
      if ((n <= 50 & x %in% c(n - 1, n - 2)) | (n >= 51 & x %in% c(n - (1:3)))) {
        ci_upr <- 1 - 0.5 * stats::qchisq(
          alpha,
          2 * (n - x)
        ) / n
      } else {
        ci_upr <- min(1, term1 + term2)
      }
    },
    `modified jeffreys` = {
      if (x == n) {
        ci_lwr <- (alpha / 2)^(1 / n)
      } else {
        if (x <= 1) {
          ci_lwr <- 0
        } else {
          ci_lwr <- stats::qbeta(
            alpha / 2,
            x + 0.5, n - x + 0.5
          )
        }
      }
      if (x == 0) {
        ci_upr <- 1 - (alpha / 2)^(1 / n)
      } else {
        if (x >= n - 1) {
          ci_upr <- 1
        } else {
          ci_upr <- stats::qbeta(1 - alpha / 2, x + 0.5, n - x + 0.5)
        }
      }
    },
    `clopper-pearson` = {
      ci_lwr <- stats::qbeta(alpha / 2, x, n - x + 1)
      ci_upr <- stats::qbeta(1 - alpha / 2, x + 1, n - x)
    },
    arcsine = {
      p_tilde <- (x + 0.375) / (n + 0.75)
      est <- p_tilde
      ci_lwr <- sin(asin(sqrt(p_tilde)) - 0.5 * kappa / sqrt(n))^2
      ci_upr <- sin(asin(sqrt(p_tilde)) + 0.5 * kappa / sqrt(n))^2
    },
    logit = {
      lambda_hat <- log(x / (n - x))
      V_hat <- n / (x * (n - x)) # nolint
      lambda_lower <- lambda_hat - kappa * sqrt(V_hat)
      lambda_upper <- lambda_hat + kappa * sqrt(V_hat)
      ci_lwr <- exp(lambda_lower) / (1 + exp(lambda_lower))
      ci_upr <- exp(lambda_upper) / (1 + exp(lambda_upper))
    },
    witting = {
      set.seed(rand)
      x_tilde <- x + stats::runif(1, min = 0, max = 1)
      pbinom_abscont <- function(q, size, prob) {
        v <- trunc(q)
        term1 <- stats::pbinom(v - 1, size = size, prob = prob)
        term2 <- (q - v) * stats::dbinom(v, size = size, prob = prob)
        return(term1 + term2)
      }
      qbinom_abscont <- function(p, size, x) {
        fun <- function(prob, size, x, p) {
          pbinom_abscont(x, size, prob) - p
        }
        stats::uniroot(fun,
          interval = c(0, 1), size = size,
          x = x, p = p
        )$root
      }
      ci_lwr <- qbinom_abscont(1 - alpha, size = n, x = x_tilde)
      ci_upr <- qbinom_abscont(alpha, size = n, x = x_tilde)
    },
    pratt = {
      if (x == 0) {
        ci_lwr <- 0
        ci_upr <- 1 - alpha^(1 / n)
      } else if (x == 1) {
        ci_lwr <- 1 - (1 - alpha / 2)^(1 / n)
        ci_upr <- 1 - (alpha / 2)^(1 / n)
      } else if (x == (n - 1)) {
        ci_lwr <- (alpha / 2)^(1 / n)
        ci_upr <- (1 - alpha / 2)^(1 / n)
      } else if (x == n) {
        ci_lwr <- alpha^(1 / n)
        ci_upr <- 1
      } else {
        z <- stats::qnorm(1 - alpha / 2)
        A <- ((x + 1) / (n - x))^2 # nolint
        B <- 81 * (x + 1) * (n - x) - 9 * n - 8 # nolint
        C <- (0 - 3) * z * sqrt(9 * (x + 1) * (n - x) * (9 * n + 5 - z^2) + n + 1) # nolint
        D <- 81 * (x + 1)^2 - 9 * (x + 1) * (2 + z^2) + 1 # nolint
        E <- 1 + A * ((B + C) / D)^3 # nolint
        ci_upr <- 1 / E
        A <- (x / (n - x - 1))^2 # nolint
        B <- 81 * x * (n - x - 1) - 9 * n - 8 # nolint
        C <- 3 * z * sqrt(9 * x * (n - x - 1) * (9 * n + 5 - z^2) + n + 1) # nolint
        D <- 81 * x^2 - 9 * x * (2 + z^2) + 1 # nolint
        E <- 1 + A * ((B + C) / D)^3 # nolint
        ci_lwr <- 1 / E
      }
    },
    midp = {
      f_low <- function(pi, x, n) {
        1 / 2 * stats::dbinom(x, size = n, prob = pi) + stats::pbinom(x,
          size = n, prob = pi, lower.tail = FALSE
        ) -
          (1 - conf.level) / 2
      }
      f_up <- function(pi, x, n) {
        1 / 2 * stats::dbinom(x, size = n, prob = pi) + stats::pbinom(x - 1, size = n, prob = pi) - (1 - conf.level) / 2
      }
      ci_lwr <- 0
      ci_upr <- 1
      if (x != 0) {
        ci_lwr <- stats::uniroot(f_low,
          interval = c(0, p_hat),
          x = x, n = n
        )$root
      }
      if (x != n) {
        ci_upr <- stats::uniroot(f_up, interval = c(
          p_hat,
          1
        ), x = x, n = n)$root
      }
    },
    lik = {
      ci_lwr <- 0
      ci_upr <- 1
      z <- stats::qnorm(1 - alpha * 0.5)
      tol <- .Machine$double.eps^0.5
      BinDev <- function(y, x, mu, wt, bound = 0, tol = .Machine$double.eps^0.5, # nolint
                         ...) {
        ll_y <- ifelse(y %in% c(0, 1), 0, stats::dbinom(x, wt,
          y,
          log = TRUE
        ))
        ll_mu <- ifelse(mu %in% c(0, 1), 0, stats::dbinom(x,
          wt, mu,
          log = TRUE
        ))
        res <- ifelse(abs(y - mu) < tol, 0, sign(y - mu) * sqrt(-2 * (ll_y - ll_mu)))
        return(res - bound)
      }
      if (x != 0 && tol < p_hat) {
        ci_lwr <- if (BinDev(
          tol, x, p_hat, n, -z,
          tol
        ) <= 0) {
          stats::uniroot(
            f = BinDev, interval = c(tol, if (p_hat < tol || p_hat == 1) {
              1 - tol
            } else {
              p_hat
            }), bound = -z,
            x = x, mu = p_hat, wt = n
          )$root
        }
      }
      if (x != n && p_hat < (1 - tol)) {
        ci_upr <- if (
          BinDev(y = 1 - tol, x = x, mu = ifelse(p_hat > 1 - tol, tol, p_hat), wt = n, bound = z, tol = tol) < 0) { # nolint
          ci_lwr <- if (BinDev(
            tol, x, if (p_hat < tol || p_hat == 1) {
              1 - tol
            } else {
              p_hat
            }, n,
            -z, tol
          ) <= 0) {
            stats::uniroot(
              f = BinDev, interval = c(tol, p_hat),
              bound = -z, x = x, mu = p_hat, wt = n
            )$root
          }
        } else {
          stats::uniroot(
            f = BinDev, interval = c(if (p_hat > 1 - tol) {
              tol
            } else {
              p_hat
            }, 1 - tol), bound = z,
            x = x, mu = p_hat, wt = n
          )$root
        }
      }
    },
    blaker = {
      acceptbin <- function(x, n, p) {
        p1 <- 1 - stats::pbinom(x - 1, n, p)
        p2 <- stats::pbinom(x, n, p)
        a1 <- p1 + stats::pbinom(stats::qbinom(p1, n, p) - 1, n, p)
        a2 <- p2 + 1 - stats::pbinom(
          stats::qbinom(1 - p2, n, p), n,
          p
        )
        return(min(a1, a2))
      }
      ci_lwr <- 0
      ci_upr <- 1
      if (x != 0) {
        ci_lwr <- stats::qbeta((1 - conf.level) / 2, x, n - x + 1)
        while (acceptbin(x, n, ci_lwr + tol) < (1 - conf.level)) {
          ci_lwr <- ci_lwr + tol
        }
      }
      if (x != n) {
        ci_upr <- stats::qbeta(1 - (1 - conf.level) / 2, x + 1, n - x)
        while (acceptbin(x, n, ci_upr - tol) < (1 - conf.level)) {
          ci_upr <- ci_upr - tol
        }
      }
    }
    )
    ci <- c(est = est, lwr.ci = max(0, ci_lwr), upr.ci = min(
      1,
      ci_upr
    ))
    if (sides == "left") {
      ci[3] <- 1
    } else if (sides == "right") {
      ci[2] <- 0
    }
    return(ci)
  }
  lst <- list(
    x = x, n = n, conf.level = conf.level, sides = sides,
    method = method, rand = rand
  )
  maxdim <- max(unlist(lapply(lst, length)))
  lgp <- lapply(lst, rep, length.out = maxdim)
  lgn <- h_recycle(x = if (is.null(names(x))) {
    paste("x", seq_along(x), sep = ".")
  } else {
    names(x)
  }, n = if (is.null(names(n))) {
    paste("n", seq_along(n), sep = ".")
  } else {
    names(n)
  }, conf.level = conf.level, sides = sides, method = method)
  xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) {
    length(unique(x)) !=
      1
  })]), 1, paste, collapse = ":")
  res <- t(sapply(1:maxdim, function(i) {
    iBinomCI(
      x = lgp$x[i],
      n = lgp$n[i], conf.level = lgp$conf.level[i], sides = lgp$sides[i],
      method = lgp$method[i], rand = lgp$rand[i]
    )
  }))
  colnames(res)[1] <- c("est")
  rownames(res) <- xn
  return(res)
}

#' Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Patient count and fraction for laboratory events (worsen from baseline) shift table.
#'
#' @inheritParams argument_convention
#' @param variables (named `list` of `string`)\cr list of additional analysis variables including:
#'   * `id` (`string`)\cr subject variable name.
#'   * `baseline_var` (`string`)\cr name of the data column containing baseline toxicity variable.
#'   * `direction_var` (`string`)\cr see `direction_var` for more details.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("abnormal_by_worst_grade_worsen")`
#'   to see all available statistics.
#'
#' @seealso Relevant helper functions [h_adlb_worsen()] and [h_worsen_counter()]
#'
#' @name abnormal_by_worst_grade_worsen
#' @order 1
NULL

#' Helper Function to Prepare `ADLB` with Worst Labs
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to prepare a `df` for generate the patient count shift table
#'
#' @param adlb (`data.frame`)\cr `ADLB` dataframe
#' @param worst_flag_low (named `vector`)\cr Worst low post-baseline lab grade flag variable
#' @param worst_flag_high (named `vector`)\cr Worst high post-baseline lab grade flag variable
#' @param direction_var (`string`)\cr Direction variable specifying the direction of the shift table of interest.
#'   Only lab records flagged by `L`, `H` or `B` are included in the shift table.
#'   * `L`: low direction only
#'   * `H`: high direction only
#'   * `B`: both low and high directions
#'
#' @return `h_adlb_worsen()` returns the `adlb` `data.frame` containing only the
#'   worst labs specified according to `worst_flag_low` or `worst_flag_high` for the
#'   direction specified according to `direction_var`. For instance, for a lab that is
#'   needed for the low direction only, only records flagged by `worst_flag_low` are
#'   selected. For a lab that is needed for both low and high directions, the worst
#'   low records are selected for the low direction, and the worst high record are selected
#'   for the high direction.
#'
#' @seealso [abnormal_by_worst_grade_worsen]
#'
#' @examples
#' library(dplyr)
#'
#' # The direction variable, GRADDR, is based on metadata
#' adlb <- tern_ex_adlb %>%
#'   mutate(
#'     GRADDR = case_when(
#'       PARAMCD == "ALT" ~ "B",
#'       PARAMCD == "CRP" ~ "L",
#'       PARAMCD == "IGA" ~ "H"
#'     )
#'   ) %>%
#'   filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
#'
#' df <- h_adlb_worsen(
#'   adlb,
#'   worst_flag_low = c("WGRLOFL" = "Y"),
#'   worst_flag_high = c("WGRHIFL" = "Y"),
#'   direction_var = "GRADDR"
#' )
#'
#' @export
h_adlb_worsen <- function(adlb,
                          worst_flag_low = NULL,
                          worst_flag_high = NULL,
                          direction_var) {
  checkmate::assert_string(direction_var)
  checkmate::assert_subset(as.character(unique(adlb[[direction_var]])), c("B", "L", "H"))
  assert_df_with_variables(adlb, list("Col" = direction_var))

  if (any(unique(adlb[[direction_var]]) == "H")) {
    assert_df_with_variables(adlb, list("High" = names(worst_flag_high)))
  }

  if (any(unique(adlb[[direction_var]]) == "L")) {
    assert_df_with_variables(adlb, list("Low" = names(worst_flag_low)))
  }

  if (any(unique(adlb[[direction_var]]) == "B")) {
    assert_df_with_variables(
      adlb,
      list(
        "Low" = names(worst_flag_low),
        "High" = names(worst_flag_high)
      )
    )
  }

  # extract patients with worst post-baseline lab, either low or high or both
  worst_flag <- c(worst_flag_low, worst_flag_high)
  col_names <- names(worst_flag)
  filter_values <- worst_flag
  temp <- Map(
    function(x, y) which(adlb[[x]] == y),
    col_names,
    filter_values
  )
  position_satisfy_filters <- Reduce(union, temp)

  # select variables of interest
  adlb_f <- adlb[position_satisfy_filters, ]

  # generate subsets for different directionality
  adlb_f_h <- adlb_f[which(adlb_f[[direction_var]] == "H"), ]
  adlb_f_l <- adlb_f[which(adlb_f[[direction_var]] == "L"), ]
  adlb_f_b <- adlb_f[which(adlb_f[[direction_var]] == "B"), ]

  # for labs requiring both high and low, data is duplicated and will be stacked on top of each other
  adlb_f_b_h <- adlb_f_b
  adlb_f_b_l <- adlb_f_b

  # extract data with worst lab
  if (!is.null(worst_flag_high) && !is.null(worst_flag_low)) {
    # change H to High, L to Low
    adlb_f_h[[direction_var]] <- rep("High", nrow(adlb_f_h))
    adlb_f_l[[direction_var]] <- rep("Low", nrow(adlb_f_l))

    # change, B to High and Low
    adlb_f_b_h[[direction_var]] <- rep("High", nrow(adlb_f_b_h))
    adlb_f_b_l[[direction_var]] <- rep("Low", nrow(adlb_f_b_l))

    adlb_out_h <- adlb_f_h[which(adlb_f_h[[names(worst_flag_high)]] == worst_flag_high), ]
    adlb_out_b_h <- adlb_f_b_h[which(adlb_f_b_h[[names(worst_flag_high)]] == worst_flag_high), ]
    adlb_out_l <- adlb_f_l[which(adlb_f_l[[names(worst_flag_low)]] == worst_flag_low), ]
    adlb_out_b_l <- adlb_f_b_l[which(adlb_f_b_l[[names(worst_flag_low)]] == worst_flag_low), ]

    out <- rbind(adlb_out_h, adlb_out_b_h, adlb_out_l, adlb_out_b_l)
  } else if (!is.null(worst_flag_high)) {
    adlb_f_h[[direction_var]] <- rep("High", nrow(adlb_f_h))
    adlb_f_b_h[[direction_var]] <- rep("High", nrow(adlb_f_b_h))

    adlb_out_h <- adlb_f_h[which(adlb_f_h[[names(worst_flag_high)]] == worst_flag_high), ]
    adlb_out_b_h <- adlb_f_b_h[which(adlb_f_b_h[[names(worst_flag_high)]] == worst_flag_high), ]

    out <- rbind(adlb_out_h, adlb_out_b_h)
  } else if (!is.null(worst_flag_low)) {
    adlb_f_l[[direction_var]] <- rep("Low", nrow(adlb_f_l))
    adlb_f_b_l[[direction_var]] <- rep("Low", nrow(adlb_f_b_l))

    adlb_out_l <- adlb_f_l[which(adlb_f_l[[names(worst_flag_low)]] == worst_flag_low), ]
    adlb_out_b_l <- adlb_f_b_l[which(adlb_f_b_l[[names(worst_flag_low)]] == worst_flag_low), ]

    out <- rbind(adlb_out_l, adlb_out_b_l)
  }

  # label
  formatters::var_labels(out) <- formatters::var_labels(adlb_f, fill = FALSE)
  # NA
  out
}

#' Helper Function to Analyze Patients for [s_count_abnormal_lab_worsen_by_baseline()]
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to count the number of patients and the fraction of patients according to
#' highest post-baseline lab grade variable `.var`, baseline lab grade variable `baseline_var`,
#' and the direction of interest specified in `direction_var`.
#'
#' @inheritParams argument_convention
#' @inheritParams h_adlb_worsen
#' @param baseline_var (`string`)\cr baseline lab grade variable
#'
#' @return `h_worsen_counter()` returns the counts and fraction of patients
#'   whose worst post-baseline lab grades are worse than their baseline grades, for
#'   post-baseline worst grades "1", "2", "3", "4" and "Any".
#'
#' @seealso [abnormal_by_worst_grade_worsen]
#'
#' @examples
#' library(dplyr)
#'
#' # The direction variable, GRADDR, is based on metadata
#' adlb <- tern_ex_adlb %>%
#'   mutate(
#'     GRADDR = case_when(
#'       PARAMCD == "ALT" ~ "B",
#'       PARAMCD == "CRP" ~ "L",
#'       PARAMCD == "IGA" ~ "H"
#'     )
#'   ) %>%
#'   filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
#'
#' df <- h_adlb_worsen(
#'   adlb,
#'   worst_flag_low = c("WGRLOFL" = "Y"),
#'   worst_flag_high = c("WGRHIFL" = "Y"),
#'   direction_var = "GRADDR"
#' )
#'
#' # `h_worsen_counter`
#' h_worsen_counter(
#'   df %>% filter(PARAMCD == "CRP" & GRADDR == "Low"),
#'   id = "USUBJID",
#'   .var = "ATOXGR",
#'   baseline_var = "BTOXGR",
#'   direction_var = "GRADDR"
#' )
#'
#' @export
h_worsen_counter <- function(df, id, .var, baseline_var, direction_var) {
  checkmate::assert_string(id)
  checkmate::assert_string(.var)
  checkmate::assert_string(baseline_var)
  checkmate::assert_scalar(unique(df[[direction_var]]))
  checkmate::assert_subset(unique(df[[direction_var]]), c("High", "Low"))
  assert_df_with_variables(df, list(val = c(id, .var, baseline_var, direction_var)))

  # remove post-baseline missing
  df <- df[df[[.var]] != "<Missing>", ]

  # obtain directionality
  direction <- unique(df[[direction_var]])

  if (direction == "Low") {
    grade <- -1:-4
    worst_grade <- -4
  } else if (direction == "High") {
    grade <- 1:4
    worst_grade <- 4
  }

  if (nrow(df) > 0) {
    by_grade <- lapply(grade, function(i) {
      # filter baseline values that is less than i or <Missing>
      df_temp <- df[df[[baseline_var]] %in% c((i + sign(i) * -1):(-1 * worst_grade), "<Missing>"), ]
      # num: number of patients with post-baseline worst lab equal to i
      num <- length(unique(df_temp[df_temp[[.var]] %in% i, id, drop = TRUE]))
      # denom: number of patients with baseline values less than i or <missing> and post-baseline in the same direction
      denom <- length(unique(df_temp[[id]]))
      rm(df_temp)
      c(num = num, denom = denom)
    })
  } else {
    by_grade <- lapply(1, function(i) {
      c(num = 0, denom = 0)
    })
  }

  names(by_grade) <- as.character(seq_along(by_grade))

  # baseline grade less 4 or missing
  df_temp <- df[!df[[baseline_var]] %in% worst_grade, ]

  # denom: number of patients with baseline values less than 4 or <missing> and post-baseline in the same direction
  denom <- length(unique(df_temp[, id, drop = TRUE]))

  # condition 1: missing baseline and in the direction of abnormality
  con1 <- which(df_temp[[baseline_var]] == "<Missing>" & df_temp[[.var]] %in% grade)
  df_temp_nm <- df_temp[which(df_temp[[baseline_var]] != "<Missing>" & df_temp[[.var]] %in% grade), ]

  # condition 2: if post-baseline values are present then post-baseline values must be worse than baseline
  if (direction == "Low") {
    con2 <- which(as.numeric(as.character(df_temp_nm[[.var]])) < as.numeric(as.character(df_temp_nm[[baseline_var]])))
  } else {
    con2 <- which(as.numeric(as.character(df_temp_nm[[.var]])) > as.numeric(as.character(df_temp_nm[[baseline_var]])))
  }

  # number of patients satisfy either conditions 1 or 2
  num <- length(unique(df_temp[union(con1, con2), id, drop = TRUE]))

  list(fraction = c(by_grade, list("Any" = c(num = num, denom = denom))))
}

#' @describeIn abnormal_by_worst_grade_worsen Statistics function for patients whose worst post-baseline
#'   lab grades are worse than their baseline grades.
#'
#' @return
#' * `s_count_abnormal_lab_worsen_by_baseline()` returns the counts and fraction of patients whose worst
#'   post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades
#'   "1", "2", "3", "4" and "Any".
#'
#' @keywords internal
s_count_abnormal_lab_worsen_by_baseline <- function(df, # nolint
                                                    .var = "ATOXGR",
                                                    variables = list(
                                                      id = "USUBJID",
                                                      baseline_var = "BTOXGR",
                                                      direction_var = "GRADDR"
                                                    )) {
  checkmate::assert_string(.var)
  checkmate::assert_set_equal(names(variables), c("id", "baseline_var", "direction_var"))
  checkmate::assert_string(variables$id)
  checkmate::assert_string(variables$baseline_var)
  checkmate::assert_string(variables$direction_var)
  assert_df_with_variables(df, c(aval = .var, variables[1:3]))
  assert_list_of_variables(variables)

  h_worsen_counter(df, variables$id, .var, variables$baseline_var, variables$direction_var)
}


#' @describeIn abnormal_by_worst_grade_worsen Formatted analysis function which is used as `afun`
#'   in `count_abnormal_lab_worsen_by_baseline()`.
#'
#' @return
#' * `a_count_abnormal_lab_worsen_by_baseline()` returns the corresponding list with
#'   formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_lab_worsen_by_baseline <- make_afun( # nolint
  s_count_abnormal_lab_worsen_by_baseline,
  .formats = c(fraction = format_fraction),
  .ungroup_stats = "fraction"
)

#' @describeIn abnormal_by_worst_grade_worsen Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_lab_worsen_by_baseline()` returns a layout object suitable for passing to further layouting
#'   functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted
#'   rows containing the statistics from `s_count_abnormal_lab_worsen_by_baseline()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' # The direction variable, GRADDR, is based on metadata
#' adlb <- tern_ex_adlb %>%
#'   mutate(
#'     GRADDR = case_when(
#'       PARAMCD == "ALT" ~ "B",
#'       PARAMCD == "CRP" ~ "L",
#'       PARAMCD == "IGA" ~ "H"
#'     )
#'   ) %>%
#'   filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
#'
#' df <- h_adlb_worsen(
#'   adlb,
#'   worst_flag_low = c("WGRLOFL" = "Y"),
#'   worst_flag_high = c("WGRHIFL" = "Y"),
#'   direction_var = "GRADDR"
#' )
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   add_colcounts() %>%
#'   split_rows_by("PARAMCD") %>%
#'   split_rows_by("GRADDR") %>%
#'   count_abnormal_lab_worsen_by_baseline(
#'     var = "ATOXGR",
#'     variables = list(
#'       id = "USUBJID",
#'       baseline_var = "BTOXGR",
#'       direction_var = "GRADDR"
#'     )
#'   ) %>%
#'   append_topleft("Direction of Abnormality") %>%
#'   build_table(df = df, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_abnormal_lab_worsen_by_baseline <- function(lyt, # nolint
                                                  var,
                                                  variables = list(
                                                    id = "USUBJID",
                                                    baseline_var = "BTOXGR",
                                                    direction_var = "GRADDR"
                                                  ),
                                                  na_str = default_na_str(),
                                                  nested = TRUE,
                                                  ...,
                                                  table_names = NULL,
                                                  .stats = NULL,
                                                  .formats = NULL,
                                                  .labels = NULL,
                                                  .indent_mods = NULL) {
  checkmate::assert_string(var)

  extra_args <- list(variables = variables, ...)

  afun <- make_afun(
    a_count_abnormal_lab_worsen_by_baseline,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  lyt <- analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = "hidden"
  )

  lyt
}

#' Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions which are documented here separately to not confuse the user
#' when reading about the user-facing functions.
#'
#' @inheritParams survival_biomarkers_subgroups
#' @inheritParams argument_convention
#' @inheritParams fit_coxreg_multivar
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte, fill = FALSE)
#'
#' adtte_f <- adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' @name h_survival_biomarkers_subgroups
NULL

#' @describeIn h_survival_biomarkers_subgroups helps with converting the "survival" function variable list
#'   to the "Cox regression" variable list. The reason is that currently there is an inconsistency between the variable
#'   names accepted by `extract_survival_subgroups()` and `fit_coxreg_multivar()`.
#'
#' @param biomarker (`string`)\cr the name of the biomarker variable.
#'
#' @return
#' * `h_surv_to_coxreg_variables()` returns a named `list` of elements `time`, `event`, `arm`,
#'   `covariates`, and `strata`.
#'
#' @examples
#' # This is how the variable list is converted internally.
#' h_surv_to_coxreg_variables(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "EVNT",
#'     covariates = c("A", "B"),
#'     strata = "D"
#'   ),
#'   biomarker = "AGE"
#' )
#'
#' @export
h_surv_to_coxreg_variables <- function(variables, biomarker) {
  checkmate::assert_list(variables)
  checkmate::assert_string(variables$tte)
  checkmate::assert_string(variables$is_event)
  checkmate::assert_string(biomarker)
  list(
    time = variables$tte,
    event = variables$is_event,
    arm = biomarker,
    covariates = variables$covariates,
    strata = variables$strata
  )
}

#' @describeIn h_survival_biomarkers_subgroups prepares estimates for number of events, patients and median survival
#'   times, as well as hazard ratio estimates, confidence intervals and p-values, for multiple biomarkers
#'   in a given single data set.
#'   `variables` corresponds to names of variables found in `data`, passed as a named list and requires elements
#'   `tte`, `is_event`, `biomarkers` (vector of continuous biomarker variables) and optionally `subgroups` and `strata`.
#'
#' @return
#' * `h_coxreg_mult_cont_df()` returns a `data.frame` containing estimates and statistics for the selected biomarkers.
#'
#' @examples
#' # For a single population, estimate separately the effects
#' # of two biomarkers.
#' df <- h_coxreg_mult_cont_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adtte_f
#' )
#' df
#'
#' # If the data set is empty, still the corresponding rows with missings are returned.
#' h_coxreg_mult_cont_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "REGION1",
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adtte_f[NULL, ]
#' )
#'
#' @export
h_coxreg_mult_cont_df <- function(variables,
                                  data,
                                  control = control_coxreg()) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_coxreg_mult_cont_df() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  assert_df_with_variables(data, variables)
  checkmate::assert_list(control, names = "named")
  checkmate::assert_character(variables$biomarkers, min.len = 1, any.missing = FALSE)
  conf_level <- control[["conf_level"]]
  pval_label <- paste0(
    # the regex capitalizes the first letter of the string / senetence.
    "p-value (", gsub("(^[a-z])", "\\U\\1", trimws(control[["pval_method"]]), perl = TRUE), ")"
  )
  # If there is any data, run model, otherwise return empty results.
  if (nrow(data) > 0) {
    bm_cols <- match(variables$biomarkers, names(data))
    l_result <- lapply(variables$biomarkers, function(bm) {
      coxreg_list <- fit_coxreg_multivar(
        variables = h_surv_to_coxreg_variables(variables, bm),
        data = data,
        control = control
      )
      result <- do.call(
        h_coxreg_multivar_extract,
        c(list(var = bm), coxreg_list[c("mod", "data", "control")])
      )
      data_fit <- as.data.frame(as.matrix(coxreg_list$mod$y))
      data_fit$status <- as.logical(data_fit$status)
      median <- s_surv_time(
        df = data_fit,
        .var = "time",
        is_event = "status"
      )$median
      data.frame(
        # Dummy column needed downstream to create a nested header.
        biomarker = bm,
        biomarker_label = formatters::var_labels(data[bm], fill = TRUE),
        n_tot = coxreg_list$mod$n,
        n_tot_events = coxreg_list$mod$nevent,
        median = as.numeric(median),
        result[1L, c("hr", "lcl", "ucl")],
        conf_level = conf_level,
        pval = result[1L, "pval"],
        pval_label = pval_label,
        stringsAsFactors = FALSE
      )
    })
    do.call(rbind, args = c(l_result, make.row.names = FALSE))
  } else {
    data.frame(
      biomarker = variables$biomarkers,
      biomarker_label = formatters::var_labels(data[variables$biomarkers], fill = TRUE),
      n_tot = 0L,
      n_tot_events = 0L,
      median = NA,
      hr = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      pval = NA,
      pval_label = pval_label,
      row.names = seq_along(variables$biomarkers),
      stringsAsFactors = FALSE
    )
  }
}

#' @describeIn h_survival_biomarkers_subgroups prepares a single sub-table given a `df_sub` containing
#'   the results for a single biomarker.
#'
#' @param df (`data.frame`)\cr results for a single biomarker, as part of what is
#'   returned by [extract_survival_biomarkers()] (it needs a couple of columns which are
#'   added by that high-level function relative to what is returned by [h_coxreg_mult_cont_df()],
#'   see the example).
#'
#' @return
#' * `h_tab_surv_one_biomarker()` returns an `rtables` table object with the given statistics arranged in columns.
#'
#' @examples
#' # Starting from above `df`, zoom in on one biomarker and add required columns.
#' df1 <- df[1, ]
#' df1$subgroup <- "All patients"
#' df1$row_type <- "content"
#' df1$var <- "ALL"
#' df1$var_label <- "All patients"
#' h_tab_surv_one_biomarker(
#'   df1,
#'   vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
#'   time_unit = "days"
#' )
#'
#' @export
h_tab_surv_one_biomarker <- function(df,
                                     vars,
                                     time_unit,
                                     na_str = default_na_str(),
                                     .indent_mods = 0L,
                                     ...) {
  afuns <- a_survival_subgroups(na_str = na_str)[vars]
  colvars <- d_survival_subgroups_colvars(
    vars,
    conf_level = df$conf_level[1],
    method = df$pval_label[1],
    time_unit = time_unit
  )
  h_tab_one_biomarker(
    df = df,
    afuns = afuns,
    colvars = colvars,
    na_str = na_str,
    .indent_mods = .indent_mods,
    ...
  )
}

#' Control Function for `CoxPH` Model
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for `CoxPH` model, typically used internally to specify
#' details of `CoxPH` model for [s_coxph_pairwise()]. `conf_level` refers to Hazard Ratio estimation.
#'
#' @inheritParams argument_convention
#' @param pval_method (`string`)\cr p-value method for testing hazard ratio = 1.
#'   Default method is `"log-rank"`, can also be set to `"wald"` or `"likelihood"`.
#' @param ties (`string`)\cr specifying the method for tie handling. Default is `"efron"`,
#'   can also be set to `"breslow"` or `"exact"`. See more in [survival::coxph()].
#'
#' @return A list of components with the same names as the arguments
#'
#' @export
control_coxph <- function(pval_method = c("log-rank", "wald", "likelihood"),
                          ties = c("efron", "breslow", "exact"),
                          conf_level = 0.95) {
  pval_method <- match.arg(pval_method)
  ties <- match.arg(ties)
  assert_proportion_value(conf_level)

  list(pval_method = pval_method, ties = ties, conf_level = conf_level)
}

#' Control Function for `survfit` Model for Survival Time
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for `survfit` model, typically used internally to specify
#' details of `survfit` model for [s_surv_time()]. `conf_level` refers to survival time estimation.
#'
#' @inheritParams argument_convention
#' @param conf_type (`string`)\cr confidence interval type. Options are "plain" (default), "log", "log-log",
#'   see more in [survival::survfit()]. Note option "none" is no longer supported.
#' @param quantiles (`numeric`)\cr of length two to specify the quantiles of survival time.
#'
#' @return A list of components with the same names as the arguments
#'
#' @export
control_surv_time <- function(conf_level = 0.95,
                              conf_type = c("plain", "log", "log-log"),
                              quantiles = c(0.25, 0.75)) {
  conf_type <- match.arg(conf_type)
  checkmate::assert_numeric(quantiles, lower = 0, upper = 1, len = 2, unique = TRUE, sorted = TRUE)
  nullo <- lapply(quantiles, assert_proportion_value)
  assert_proportion_value(conf_level)
  list(conf_level = conf_level, conf_type = conf_type, quantiles = quantiles)
}

#' Control Function for `survfit` Model for Patient's Survival Rate at time point
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for `survfit` model, typically used internally to specify
#' details of `survfit` model for [s_surv_timepoint()]. `conf_level` refers to patient risk estimation at a time point.
#'
#' @inheritParams argument_convention
#' @inheritParams control_surv_time
#'
#' @return A list of components with the same names as the arguments
#'
#' @export
control_surv_timepoint <- function(conf_level = 0.95,
                                   conf_type = c("plain", "log", "log-log")) {
  conf_type <- match.arg(conf_type)
  assert_proportion_value(conf_level)
  list(
    conf_level = conf_level,
    conf_type = conf_type
  )
}

#' Odds Ratio Estimation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Compares bivariate responses between two groups in terms of odds ratios
#' along with a confidence interval.
#'
#' @inheritParams split_cols_by_groups
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_odds_ratio")`
#'   to see available statistics for this function.
#'
#' @details This function uses either logistic regression for unstratified
#'   analyses, or conditional logistic regression for stratified analyses.
#'   The Wald confidence interval with the specified confidence level is
#'   calculated.
#'
#' @note For stratified analyses, there is currently no implementation for conditional
#'   likelihood confidence intervals, therefore the likelihood confidence interval is not
#'   yet available as an option. Besides, when `rsp` contains only responders or non-responders,
#'   then the result values will be `NA`, because no odds ratio estimation is possible.
#'
#' @seealso Relevant helper function [h_odds_ratio()].
#'
#' @name odds_ratio
#' @order 1
NULL

#' @describeIn odds_ratio Statistics function which estimates the odds ratio
#'   between a treatment and a control. A `variables` list with `arm` and `strata`
#'   variable names must be passed if a stratified analysis is required.
#'
#' @return
#' * `s_odds_ratio()` returns a named list with the statistics `or_ci`
#'   (containing `est`, `lcl`, and `ucl`) and `n_tot`.
#'
#' @examples
#' # Unstratified analysis.
#' s_odds_ratio(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   .df_row = dta
#' )
#'
#' # Stratified analysis.
#' s_odds_ratio(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   .df_row = dta,
#'   variables = list(arm = "grp", strata = "strata")
#' )
#'
#' @export
s_odds_ratio <- function(df,
                         .var,
                         .ref_group,
                         .in_ref_col,
                         .df_row,
                         variables = list(arm = NULL, strata = NULL),
                         conf_level = 0.95,
                         groups_list = NULL) {
  y <- list(or_ci = "", n_tot = "")

  if (!.in_ref_col) {
    assert_proportion_value(conf_level)
    assert_df_with_variables(df, list(rsp = .var))
    assert_df_with_variables(.ref_group, list(rsp = .var))

    if (is.null(variables$strata)) {
      data <- data.frame(
        rsp = c(.ref_group[[.var]], df[[.var]]),
        grp = factor(
          rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
          levels = c("ref", "Not-ref")
        )
      )
      y <- or_glm(data, conf_level = conf_level)
    } else {
      assert_df_with_variables(.df_row, c(list(rsp = .var), variables))

      # The group variable prepared for clogit must be synchronised with combination groups definition.
      if (is.null(groups_list)) {
        ref_grp <- as.character(unique(.ref_group[[variables$arm]]))
        trt_grp <- as.character(unique(df[[variables$arm]]))
        grp <- stats::relevel(factor(.df_row[[variables$arm]]), ref = ref_grp)
      } else {
        # If more than one level in reference col.
        reference <- as.character(unique(.ref_group[[variables$arm]]))
        grp_ref_flag <- vapply(
          X = groups_list,
          FUN.VALUE = TRUE,
          FUN = function(x) all(reference %in% x)
        )
        ref_grp <- names(groups_list)[grp_ref_flag]

        # If more than one level in treatment col.
        treatment <- as.character(unique(df[[variables$arm]]))
        grp_trt_flag <- vapply(
          X = groups_list,
          FUN.VALUE = TRUE,
          FUN = function(x) all(treatment %in% x)
        )
        trt_grp <- names(groups_list)[grp_trt_flag]

        grp <- combine_levels(.df_row[[variables$arm]], levels = reference, new_level = ref_grp)
        grp <- combine_levels(grp, levels = treatment, new_level = trt_grp)
      }

      # The reference level in `grp` must be the same as in the `rtables` column split.
      data <- data.frame(
        rsp = .df_row[[.var]],
        grp = grp,
        strata = interaction(.df_row[variables$strata])
      )
      y_all <- or_clogit(data, conf_level = conf_level)
      checkmate::assert_string(trt_grp)
      checkmate::assert_subset(trt_grp, names(y_all$or_ci))
      y$or_ci <- y_all$or_ci[[trt_grp]]
      y$n_tot <- y_all$n_tot
    }
  }

  y$or_ci <- formatters::with_label(
    x = y$or_ci,
    label = paste0("Odds Ratio (", 100 * conf_level, "% CI)")
  )

  y$n_tot <- formatters::with_label(
    x = y$n_tot,
    label = "Total n"
  )

  y
}

#' @describeIn odds_ratio Formatted analysis function which is used as `afun` in `estimate_odds_ratio()`.
#'
#' @return
#' * `a_odds_ratio()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_odds_ratio(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   .df_row = dta
#' )
#'
#' @export
a_odds_ratio <- make_afun(
  s_odds_ratio,
  .formats = c(or_ci = "xx.xx (xx.xx - xx.xx)"),
  .indent_mods = c(or_ci = 1L)
)

#' @describeIn odds_ratio Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @param ... arguments passed to `s_odds_ratio()`.
#'
#' @return
#' * `estimate_odds_ratio()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_odds_ratio()` to the table layout.
#'
#' @examples
#' set.seed(12)
#' dta <- data.frame(
#'   rsp = sample(c(TRUE, FALSE), 100, TRUE),
#'   grp = factor(rep(c("A", "B"), each = 50), levels = c("A", "B")),
#'   strata = factor(sample(c("C", "D"), 100, TRUE))
#' )
#'
#' l <- basic_table() %>%
#'   split_cols_by(var = "grp", ref_group = "B") %>%
#'   estimate_odds_ratio(vars = "rsp")
#'
#' build_table(l, df = dta)
#'
#' @export
#' @order 2
estimate_odds_ratio <- function(lyt,
                                vars,
                                variables = list(arm = NULL, strata = NULL),
                                conf_level = 0.95,
                                groups_list = NULL,
                                na_str = default_na_str(),
                                nested = TRUE,
                                ...,
                                show_labels = "hidden",
                                table_names = vars,
                                .stats = "or_ci",
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = NULL) {
  extra_args <- list(variables = variables, conf_level = conf_level, groups_list = groups_list, ...)

  afun <- make_afun(
    a_odds_ratio,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Helper Functions for Odds Ratio Estimation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions to calculate odds ratios in [estimate_odds_ratio()].
#'
#' @inheritParams argument_convention
#' @param data (`data.frame`)\cr data frame containing at least the variables `rsp` and `grp`, and optionally
#'   `strata` for [or_clogit()].
#'
#' @return A named `list` of elements `or_ci` and `n_tot`.
#'
#' @seealso [odds_ratio]
#'
#' @name h_odds_ratio
NULL

#' @describeIn h_odds_ratio Estimates the odds ratio based on [stats::glm()]. Note that there must be
#'   exactly 2 groups in `data` as specified by the `grp` variable.
#'
#' @examples
#' # Data with 2 groups.
#' data <- data.frame(
#'   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)),
#'   grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)],
#'   strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)],
#'   stringsAsFactors = TRUE
#' )
#'
#' # Odds ratio based on glm.
#' or_glm(data, conf_level = 0.95)
#'
#' @export
or_glm <- function(data, conf_level) {
  checkmate::assert_logical(data$rsp)
  assert_proportion_value(conf_level)
  assert_df_with_variables(data, list(rsp = "rsp", grp = "grp"))
  checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))

  data$grp <- as_factor_keep_attributes(data$grp)
  assert_df_with_factors(data, list(val = "grp"), min.levels = 2, max.levels = 2)
  formula <- stats::as.formula("rsp ~ grp")
  model_fit <- stats::glm(
    formula = formula, data = data,
    family = stats::binomial(link = "logit")
  )

  # Note that here we need to discard the intercept.
  or <- exp(stats::coef(model_fit)[-1])
  or_ci <- exp(
    stats::confint.default(model_fit, level = conf_level)[-1, , drop = FALSE]
  )

  values <- stats::setNames(c(or, or_ci), c("est", "lcl", "ucl"))
  n_tot <- stats::setNames(nrow(model_fit$model), "n_tot")

  list(or_ci = values, n_tot = n_tot)
}

#' @describeIn h_odds_ratio estimates the odds ratio based on [survival::clogit()]. This is done for
#'   the whole data set including all groups, since the results are not the same as when doing
#'   pairwise comparisons between the groups.
#'
#' @examples
#' # Data with 3 groups.
#' data <- data.frame(
#'   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)),
#'   grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)],
#'   strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)],
#'   stringsAsFactors = TRUE
#' )
#'
#' # Odds ratio based on stratified estimation by conditional logistic regression.
#' or_clogit(data, conf_level = 0.95)
#'
#' @export
or_clogit <- function(data, conf_level) {
  checkmate::assert_logical(data$rsp)
  assert_proportion_value(conf_level)
  assert_df_with_variables(data, list(rsp = "rsp", grp = "grp", strata = "strata"))
  checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))
  checkmate::assert_multi_class(data$strata, classes = c("factor", "character"))

  data$grp <- as_factor_keep_attributes(data$grp)
  data$strata <- as_factor_keep_attributes(data$strata)

  # Deviation from convention: `survival::strata` must be simply `strata`.
  formula <- stats::as.formula("rsp ~ grp + strata(strata)")
  model_fit <- clogit_with_tryCatch(formula = formula, data = data)

  # Create a list with one set of OR estimates and CI per coefficient, i.e.
  # comparison of one group vs. the reference group.
  coef_est <- stats::coef(model_fit)
  ci_est <- stats::confint(model_fit, level = conf_level)
  or_ci <- list()
  for (coef_name in names(coef_est)) {
    grp_name <- gsub("^grp", "", x = coef_name)
    or_ci[[grp_name]] <- stats::setNames(
      object = exp(c(coef_est[coef_name], ci_est[coef_name, , drop = TRUE])),
      nm = c("est", "lcl", "ucl")
    )
  }
  list(or_ci = or_ci, n_tot = c(n_tot = model_fit$n))
}

#' Cox Regression Helper: Interactions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Test and estimate the effect of a treatment in interaction with a covariate.
#' The effect is estimated as the HR of the tested treatment for a given level
#' of the covariate, in comparison to the treatment control.
#'
#' @inheritParams argument_convention
#' @param x (`numeric` or `factor`)\cr the values of the covariate to be tested.
#' @param effect (`string`)\cr the name of the effect to be tested and estimated.
#' @param covar (`string`)\cr the name of the covariate in the model.
#' @param mod (`coxph`)\cr the Cox regression model.
#' @param label (`string`)\cr the label to be returned as `term_label`.
#' @param control (`list`)\cr a list of controls as returned by [control_coxreg()].
#' @param ... see methods.
#'
#' @examples
#' library(survival)
#'
#' set.seed(1, kind = "Mersenne-Twister")
#'
#' # Testing dataset [survival::bladder].
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   data.frame(
#'     time = stop,
#'     status = event,
#'     armcd = as.factor(rx),
#'     covar1 = as.factor(enum),
#'     covar2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4,
#'       labels = c("F", "F", "M", "M")
#'     )
#'   )
#' )
#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#'
#' plot(
#'   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
#'   lty = 2:4,
#'   xlab = "Months",
#'   col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
#' )
#'
#' @name cox_regression_inter
NULL

#' @describeIn cox_regression_inter S3 generic helper function to determine interaction effect.
#'
#' @return
#' * `h_coxreg_inter_effect()` returns a `data.frame` of covariate interaction effects consisting of the following
#'   variables: `effect`, `term`, `term_label`, `level`, `n`, `hr`, `lcl`, `ucl`, `pval`, and `pval_inter`.
#'
#' @export
h_coxreg_inter_effect <- function(x,
                                  effect,
                                  covar,
                                  mod,
                                  label,
                                  control,
                                  ...) {
  UseMethod("h_coxreg_inter_effect", x)
}

#' @describeIn cox_regression_inter Method for `numeric` class. Estimates the interaction with a `numeric` covariate.
#'
#' @method h_coxreg_inter_effect numeric
#'
#' @param at (`list`)\cr a list with items named after the covariate, every
#'   item is a vector of levels at which the interaction should be estimated.
#'
#' @export
h_coxreg_inter_effect.numeric <- function(x,
                                          effect,
                                          covar,
                                          mod,
                                          label,
                                          control,
                                          at,
                                          ...) {
  betas <- stats::coef(mod)
  attrs <- attr(stats::terms(mod), "term.labels")
  term_indices <- grep(
    pattern = effect,
    x = attrs[!grepl("strata\\(", attrs)]
  )
  checkmate::assert_vector(term_indices, len = 2)
  betas <- betas[term_indices]
  betas_var <- diag(stats::vcov(mod))[term_indices]
  betas_cov <- stats::vcov(mod)[term_indices[1], term_indices[2]]
  xval <- if (is.null(at[[covar]])) {
    stats::median(x)
  } else {
    at[[covar]]
  }
  effect_index <- !grepl(covar, names(betas))
  coef_hat <- betas[effect_index] + xval * betas[!effect_index]
  coef_se <- sqrt(
    betas_var[effect_index] +
      xval ^ 2 * betas_var[!effect_index] + # styler: off
      2 * xval * betas_cov
  )
  q_norm <- stats::qnorm((1 + control$conf_level) / 2)
  data.frame(
    effect = "Covariate:",
    term = rep(covar, length(xval)),
    term_label = paste0("  ", xval),
    level = as.character(xval),
    n = NA,
    hr = exp(coef_hat),
    lcl = exp(coef_hat - q_norm * coef_se),
    ucl = exp(coef_hat + q_norm * coef_se),
    pval = NA,
    pval_inter = NA,
    stringsAsFactors = FALSE
  )
}

#' @describeIn cox_regression_inter Method for `factor` class. Estimate the interaction with a `factor` covariate.
#'
#' @method h_coxreg_inter_effect factor
#'
#' @param data (`data.frame`)\cr the data frame on which the model was fit.
#'
#' @export
h_coxreg_inter_effect.factor <- function(x,
                                         effect,
                                         covar,
                                         mod,
                                         label,
                                         control,
                                         data,
                                         ...) {
  lvl_given <- levels(x)
  y <- h_coxreg_inter_estimations(
    variable = effect, given = covar,
    lvl_var = levels(data[[effect]]),
    lvl_given = lvl_given,
    mod = mod,
    conf_level = 0.95
  )[[1]]

  data.frame(
    effect = "Covariate:",
    term = rep(covar, nrow(y)),
    term_label = paste0("  ", lvl_given),
    level = lvl_given,
    n = NA,
    hr = y[, "hr"],
    lcl = y[, "lcl"],
    ucl = y[, "ucl"],
    pval = NA,
    pval_inter = NA,
    stringsAsFactors = FALSE
  )
}

#' @describeIn cox_regression_inter Method for `character` class. Estimate the interaction with a `character` covariate.
#'   This makes an automatic conversion to `factor` and then forwards to the method for factors.
#'
#' @method h_coxreg_inter_effect character
#'
#' @note
#' * Automatic conversion of character to factor does not guarantee results can be generated correctly. It is
#'   therefore better to always pre-process the dataset such that factors are manually created from character
#'   variables before passing the dataset to [rtables::build_table()].
#'
#' @export
h_coxreg_inter_effect.character <- function(x,
                                            effect,
                                            covar,
                                            mod,
                                            label,
                                            control,
                                            data,
                                            ...) {
  y <- as.factor(x)

  h_coxreg_inter_effect(
    x = y,
    effect = effect,
    covar = covar,
    mod = mod,
    label = label,
    control = control,
    data = data,
    ...
  )
}

#' @describeIn cox_regression_inter A higher level function to get
#'   the results of the interaction test and the estimated values.
#'
#' @return
#' * `h_coxreg_extract_interaction()` returns the result of an interaction test and the estimated values. If
#'   no interaction, [h_coxreg_univar_extract()] is applied instead.
#'
#' @examples
#' mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder)
#' h_coxreg_extract_interaction(
#'   mod = mod, effect = "armcd", covar = "covar1", data = dta_bladder,
#'   control = control_coxreg()
#' )
#'
#' @export
h_coxreg_extract_interaction <- function(effect,
                                         covar,
                                         mod,
                                         data,
                                         at,
                                         control) {
  if (!any(attr(stats::terms(mod), "order") == 2)) {
    y <- h_coxreg_univar_extract(
      effect = effect, covar = covar, mod = mod, data = data, control = control
    )
    y$pval_inter <- NA
    y
  } else {
    test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]

    # Test the main treatment effect.
    mod_aov <- muffled_car_anova(mod, test_statistic)
    sum_anova <- broom::tidy(mod_aov)
    pval <- sum_anova[sum_anova$term == effect, ][["p.value"]]

    # Test the interaction effect.
    pval_inter <- sum_anova[grep(":", sum_anova$term), ][["p.value"]]
    covar_test <- data.frame(
      effect = "Covariate:",
      term = covar,
      term_label = unname(labels_or_names(data[covar])),
      level = "",
      n = mod$n, hr = NA, lcl = NA, ucl = NA, pval = pval,
      pval_inter = pval_inter,
      stringsAsFactors = FALSE
    )
    # Estimate the interaction.
    y <- h_coxreg_inter_effect(
      data[[covar]],
      covar = covar,
      effect = effect,
      mod = mod,
      label = unname(labels_or_names(data[covar])),
      at = at,
      control = control,
      data = data
    )
    rbind(covar_test, y)
  }
}

#' @describeIn cox_regression_inter Hazard ratio estimation in interactions.
#'
#' @param variable,given (`string`)\cr the name of variables in interaction. We seek the estimation
#'   of the levels of `variable` given the levels of `given`.
#' @param lvl_var,lvl_given (`character`)\cr corresponding levels has given by [levels()].
#' @param mod (`coxph`)\cr a fitted Cox regression model (see [survival::coxph()]).
#'
#' @details Given the cox regression investigating the effect of Arm (A, B, C; reference A)
#'   and Sex (F, M; reference Female) and the model being abbreviated: y ~ Arm + Sex + Arm:Sex.
#'   The cox regression estimates the coefficients along with a variance-covariance matrix for:
#'
#'   - b1 (arm b), b2 (arm c)
#'   - b3 (sex m)
#'   - b4 (arm b: sex m), b5 (arm c: sex m)
#'
#'   The estimation of the Hazard Ratio for arm C/sex M is given in reference
#'   to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5).
#'   The interaction coefficient is deduced by b2 + b5 while the standard error
#'   is obtained as $sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$.
#'
#' @return
#' * `h_coxreg_inter_estimations()` returns a list of matrices (one per level of variable) with rows corresponding
#'   to the combinations of `variable` and `given`, with columns:
#'   * `coef_hat`: Estimation of the coefficient.
#'   * `coef_se`: Standard error of the estimation.
#'   * `hr`: Hazard ratio.
#'   * `lcl, ucl`: Lower/upper confidence limit of the hazard ratio.
#'
#' @examples
#' mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder)
#' result <- h_coxreg_inter_estimations(
#'   variable = "armcd", given = "covar1",
#'   lvl_var = levels(dta_bladder$armcd),
#'   lvl_given = levels(dta_bladder$covar1),
#'   mod = mod, conf_level = .95
#' )
#' result
#'
#' @export
h_coxreg_inter_estimations <- function(variable,
                                       given,
                                       lvl_var,
                                       lvl_given,
                                       mod,
                                       conf_level = 0.95) {
  var_lvl <- paste0(variable, lvl_var[-1]) # [-1]: reference level
  giv_lvl <- paste0(given, lvl_given)
  design_mat <- expand.grid(variable = var_lvl, given = giv_lvl)
  design_mat <- design_mat[order(design_mat$variable, design_mat$given), ]
  design_mat <- within(
    data = design_mat,
    expr = {
      inter <- paste0(variable, ":", given)
      rev_inter <- paste0(given, ":", variable)
    }
  )
  split_by_variable <- design_mat$variable
  interaction_names <- paste(design_mat$variable, design_mat$given, sep = "/")

  mmat <- stats::model.matrix(mod)[1, ]
  mmat[!mmat == 0] <- 0

  design_mat <- apply(
    X = design_mat, MARGIN = 1, FUN = function(x) {
      mmat[names(mmat) %in% x[-which(names(x) == "given")]] <- 1
      mmat
    }
  )
  colnames(design_mat) <- interaction_names

  coef <- stats::coef(mod)
  vcov <- stats::vcov(mod)
  betas <- as.matrix(coef)
  coef_hat <- t(design_mat) %*% betas
  dimnames(coef_hat)[2] <- "coef"
  coef_se <- apply(
    design_mat, 2,
    function(x) {
      vcov_el <- as.logical(x)
      y <- vcov[vcov_el, vcov_el]
      y <- sum(y)
      y <- sqrt(y)
      return(y)
    }
  )
  q_norm <- stats::qnorm((1 + conf_level) / 2)
  y <- cbind(coef_hat, `se(coef)` = coef_se)
  y <- apply(y, 1, function(x) {
    x["hr"] <- exp(x["coef"])
    x["lcl"] <- exp(x["coef"] - q_norm * x["se(coef)"])
    x["ucl"] <- exp(x["coef"] + q_norm * x["se(coef)"])
    x
  })
  y <- t(y)
  y <- by(y, split_by_variable, identity)
  y <- lapply(y, as.matrix)
  attr(y, "details") <- paste0(
    "Estimations of ", variable,
    " hazard ratio given the level of ", given, " compared to ",
    variable, " level ", lvl_var[1], "."
  )
  y
}

#' Cox Proportional Hazards Regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Fits a Cox regression model and estimates hazard ratio to describe the effect size in a survival analysis.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("summarize_coxreg")`
#'   to see available statistics for this function.
#'
#' @details Cox models are the most commonly used methods to estimate the magnitude of
#'   the effect in survival analysis. It assumes proportional hazards: the ratio
#'   of the hazards between groups (e.g., two arms) is constant over time.
#'   This ratio is referred to as the "hazard ratio" (HR) and is one of the
#'   most commonly reported metrics to describe the effect size in survival
#'   analysis (NEST Team, 2020).
#'
#' @seealso [fit_coxreg] for relevant fitting functions, [h_cox_regression] for relevant
#'   helper functions, and [tidy_coxreg] for custom tidy methods.
#'
#' @examples
#' library(survival)
#'
#' # Testing dataset [survival::bladder].
#' set.seed(1, kind = "Mersenne-Twister")
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   tibble::tibble(
#'     TIME = stop,
#'     STATUS = event,
#'     ARM = as.factor(rx),
#'     COVAR1 = as.factor(enum) %>% formatters::with_label("A Covariate Label"),
#'     COVAR2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4, labels = c("F", "F", "M", "M")
#'     ) %>% formatters::with_label("Sex (F/M)")
#'   )
#' )
#' dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#' dta_bladder$STUDYID <- factor("X")
#'
#' u1_variables <- list(
#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
#' )
#'
#' u2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
#'
#' m1_variables <- list(
#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
#' )
#'
#' m2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
#'
#' @name cox_regression
#' @order 1
NULL

#' @describeIn cox_regression Statistics function that transforms results tabulated
#'   from [fit_coxreg_univar()] or [fit_coxreg_multivar()] into a list.
#'
#' @param model_df (`data.frame`)\cr contains the resulting model fit from a [fit_coxreg]
#'   function with tidying applied via [broom::tidy()].
#' @param .stats (`character`)\cr the name of statistics to be reported among:
#'   * `n`: number of observations (univariate only)
#'   * `hr`: hazard ratio
#'   * `ci`: confidence interval
#'   * `pval`: p-value of the treatment effect
#'   * `pval_inter`: p-value of the interaction effect between the treatment and the covariate (univariate only)
#' @param .which_vars (`character`)\cr which rows should statistics be returned for from the given model.
#'   Defaults to "all". Other options include "var_main" for main effects, `"inter"` for interaction effects,
#'   and `"multi_lvl"` for multivariate model covariate level rows. When `.which_vars` is "all" specific
#'   variables can be selected by specifying `.var_nms`.
#' @param .var_nms (`character`)\cr the `term` value of rows in `df` for which `.stats` should be returned. Typically
#'   this is the name of a variable. If using variable labels, `var` should be a vector of both the desired
#'   variable name and the variable label in that order to see all `.stats` related to that variable. When `.which_vars`
#'   is `"var_main"` `.var_nms` should be only the variable name.
#'
#' @return
#' * `s_coxreg()` returns the selected statistic for from the Cox regression model for the selected variable(s).
#'
#' @examples
#' # s_coxreg
#'
#' # Univariate
#' univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder)
#' df1 <- broom::tidy(univar_model)
#'
#' s_coxreg(model_df = df1, .stats = "hr")
#'
#' # Univariate with interactions
#' univar_model_inter <- fit_coxreg_univar(
#'   variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder
#' )
#' df1_inter <- broom::tidy(univar_model_inter)
#'
#' s_coxreg(model_df = df1_inter, .stats = "hr", .which_vars = "inter", .var_nms = "COVAR1")
#'
#' # Univariate without treatment arm - only "COVAR2" covariate effects
#' univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder)
#' df1_covs <- broom::tidy(univar_covs_model)
#'
#' s_coxreg(model_df = df1_covs, .stats = "hr", .var_nms = c("COVAR2", "Sex (F/M)"))
#'
#' # Multivariate.
#' multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder)
#' df2 <- broom::tidy(multivar_model)
#'
#' s_coxreg(model_df = df2, .stats = "pval", .which_vars = "var_main", .var_nms = "COVAR1")
#' s_coxreg(
#'   model_df = df2, .stats = "pval", .which_vars = "multi_lvl",
#'   .var_nms = c("COVAR1", "A Covariate Label")
#' )
#'
#' # Multivariate without treatment arm - only "COVAR1" main effect
#' multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder)
#' df2_covs <- broom::tidy(multivar_covs_model)
#'
#' s_coxreg(model_df = df2_covs, .stats = "hr")
#'
#' @export
s_coxreg <- function(model_df, .stats, .which_vars = "all", .var_nms = NULL) {
  assert_df_with_variables(model_df, list(term = "term", stat = .stats))
  checkmate::assert_multi_class(model_df$term, classes = c("factor", "character"))
  model_df$term <- as.character(model_df$term)
  .var_nms <- .var_nms[!is.na(.var_nms)]

  if (length(.var_nms) > 0) model_df <- model_df[model_df$term %in% .var_nms, ]
  if (.which_vars == "multi_lvl") model_df$term <- tail(.var_nms, 1)

  # We need a list with names corresponding to the stats to display of equal length to the list of stats.
  y <- split(model_df, f = model_df$term, drop = FALSE)
  y <- stats::setNames(y, nm = rep(.stats, length(y)))

  if (.which_vars == "var_main") {
    y <- lapply(y, function(x) x[1, ]) # only main effect
  } else if (.which_vars %in% c("inter", "multi_lvl")) {
    y <- lapply(y, function(x) if (nrow(y[[1]]) > 1) x[-1, ] else x) # exclude main effect
  }

  lapply(
    X = y,
    FUN = function(x) {
      z <- as.list(x[[.stats]])
      stats::setNames(z, nm = x$term_label)
    }
  )
}

#' @describeIn cox_regression Analysis function which is used as `afun` in [rtables::analyze()]
#'   and `cfun` in [rtables::summarize_row_groups()] within `summarize_coxreg()`.
#'
#' @param eff (`flag`)\cr whether treatment effect should be calculated. Defaults to `FALSE`.
#' @param var_main (`flag`)\cr whether main effects should be calculated. Defaults to `FALSE`.
#' @param na_str (`string`)\cr custom string to replace all `NA` values with. Defaults to `""`.
#' @param cache_env (`environment`)\cr an environment object used to cache the regression model in order to
#'   avoid repeatedly fitting the same model for every row in the table. Defaults to `NULL` (no caching).
#' @param varlabels (`list`)\cr a named list corresponds to the names of variables found in data, passed
#'   as a named list and corresponding to time, event, arm, strata, and covariates terms. If arm is missing
#'   from variables, then only Cox model(s) including the covariates will be fitted and the corresponding
#'   effect estimates will be tabulated later.
#'
#' @return
#' * `a_coxreg()` returns formatted [rtables::CellValue()].
#'
#' @examples
#' a_coxreg(
#'   df = dta_bladder,
#'   labelstr = "Label 1",
#'   variables = u1_variables,
#'   .spl_context = list(value = "COVAR1"),
#'   .stats = "n",
#'   .formats = "xx"
#' )
#'
#' a_coxreg(
#'   df = dta_bladder,
#'   labelstr = "",
#'   variables = u1_variables,
#'   .spl_context = list(value = "COVAR2"),
#'   .stats = "pval",
#'   .formats = "xx.xxxx"
#' )
#'
#' @export
a_coxreg <- function(df,
                     labelstr,
                     eff = FALSE,
                     var_main = FALSE,
                     multivar = FALSE,
                     variables,
                     at = list(),
                     control = control_coxreg(),
                     .spl_context,
                     .stats,
                     .formats,
                     .indent_mods = NULL,
                     na_str = "",
                     cache_env = NULL) {
  cov_no_arm <- !multivar && !"arm" %in% names(variables) && control$interaction # special case: univar no arm
  cov <- tail(.spl_context$value, 1) # current variable/covariate
  var_lbl <- formatters::var_labels(df)[cov] # check for df labels
  if (length(labelstr) > 1) {
    labelstr <- if (cov %in% names(labelstr)) labelstr[[cov]] else var_lbl # use df labels if none
  } else if (!is.na(var_lbl) && labelstr == cov && cov %in% variables$covariates) {
    labelstr <- var_lbl
  }
  if (eff || multivar || cov_no_arm) {
    control$interaction <- FALSE
  } else {
    variables$covariates <- cov
    if (var_main) control$interaction <- TRUE
  }

  if (is.null(cache_env[[cov]])) {
    if (!multivar) {
      model <- fit_coxreg_univar(variables = variables, data = df, at = at, control = control) %>% broom::tidy()
    } else {
      model <- fit_coxreg_multivar(variables = variables, data = df, control = control) %>% broom::tidy()
    }
    cache_env[[cov]] <- model
  } else {
    model <- cache_env[[cov]]
  }
  if (!multivar && !var_main) model[, "pval_inter"] <- NA_real_

  if (cov_no_arm || (!cov_no_arm && !"arm" %in% names(variables) && is.numeric(df[[cov]]))) {
    multivar <- TRUE
    if (!cov_no_arm) var_main <- TRUE
  }

  vars_coxreg <- list(which_vars = "all", var_nms = NULL)
  if (eff) {
    if (multivar && !var_main) { # multivar treatment level
      var_lbl_arm <- formatters::var_labels(df)[[variables$arm]]
      vars_coxreg[c("var_nms", "which_vars")] <- list(c(variables$arm, var_lbl_arm), "multi_lvl")
    } else { # treatment effect
      vars_coxreg["var_nms"] <- variables$arm
      if (var_main) vars_coxreg["which_vars"] <- "var_main"
    }
  } else {
    if (!multivar || (multivar && var_main && !is.numeric(df[[cov]]))) { # covariate effect/level
      vars_coxreg[c("var_nms", "which_vars")] <- list(cov, "var_main")
    } else if (multivar) { # multivar covariate level
      vars_coxreg[c("var_nms", "which_vars")] <- list(c(cov, var_lbl), "multi_lvl")
      if (var_main) model[cov, .stats] <- NA_real_
    }
    if (!multivar && !var_main && control$interaction) vars_coxreg["which_vars"] <- "inter" # interaction effect
  }
  var_vals <- s_coxreg(model, .stats, .which_vars = vars_coxreg$which_vars, .var_nms = vars_coxreg$var_nms)[[1]]
  var_names <- if (all(grepl("\\(reference = ", names(var_vals))) && labelstr != tail(.spl_context$value, 1)) {
    paste(c(labelstr, tail(strsplit(names(var_vals), " ")[[1]], 3)), collapse = " ") # "reference" main effect labels
  } else if ((!multivar && !eff && !(!var_main && control$interaction) && nchar(labelstr) > 0) ||
    (multivar && var_main && is.numeric(df[[cov]]))) { # nolint
    labelstr # other main effect labels
  } else if (multivar && !eff && !var_main && is.numeric(df[[cov]])) {
    "All" # multivar numeric covariate
  } else {
    names(var_vals)
  }
  in_rows(
    .list = var_vals, .names = var_names, .labels = var_names, .indent_mods = .indent_mods,
    .formats = stats::setNames(rep(.formats, length(var_names)), var_names),
    .format_na_strs = stats::setNames(rep(na_str, length(var_names)), var_names)
  )
}

#' @describeIn cox_regression Layout-creating function which creates a Cox regression summary table
#'   layout. This function is a wrapper for several `rtables` layouting functions. This function
#'   is a wrapper for [rtables::analyze_colvars()] and [rtables::summarize_row_groups()].
#'
#' @inheritParams fit_coxreg_univar
#' @param multivar (`flag`)\cr Defaults to `FALSE`. If `TRUE` multivariate Cox regression will run, otherwise
#'   univariate Cox regression will run.
#' @param common_var (`character`)\cr the name of a factor variable in the dataset which takes the same value
#'   for all rows. This should be created during pre-processing if no such variable currently exists.
#' @param .section_div (`character`)\cr string which should be repeated as a section divider between sections.
#'   Defaults to `NA` for no section divider. If a vector of two strings are given, the first will be used between
#'   treatment and covariate sections and the second between different covariates.
#'
#' @return
#' * `summarize_coxreg()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add a Cox regression table
#'   containing the chosen statistics to the table layout.
#'
#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()] which also take the `variables`, `data`,
#'   `at` (univariate only), and `control` arguments but return unformatted univariate and multivariate
#'   Cox regression models, respectively.
#'
#' @examples
#' # summarize_coxreg
#'
#' result_univar <- basic_table() %>%
#'   summarize_coxreg(variables = u1_variables) %>%
#'   build_table(dta_bladder)
#' result_univar
#'
#' result_univar_covs <- basic_table() %>%
#'   summarize_coxreg(
#'     variables = u2_variables,
#'   ) %>%
#'   build_table(dta_bladder)
#' result_univar_covs
#'
#' result_multivar <- basic_table() %>%
#'   summarize_coxreg(
#'     variables = m1_variables,
#'     multivar = TRUE,
#'   ) %>%
#'   build_table(dta_bladder)
#' result_multivar
#'
#' result_multivar_covs <- basic_table() %>%
#'   summarize_coxreg(
#'     variables = m2_variables,
#'     multivar = TRUE,
#'     varlabels = c("Covariate 1", "Covariate 2") # custom labels
#'   ) %>%
#'   build_table(dta_bladder)
#' result_multivar_covs
#'
#' @export
#' @order 2
summarize_coxreg <- function(lyt,
                             variables,
                             control = control_coxreg(),
                             at = list(),
                             multivar = FALSE,
                             common_var = "STUDYID",
                             .stats = c("n", "hr", "ci", "pval", "pval_inter"),
                             .formats = c(
                               n = "xx", hr = "xx.xx", ci = "(xx.xx, xx.xx)",
                               pval = "x.xxxx | (<0.0001)", pval_inter = "x.xxxx | (<0.0001)"
                             ),
                             varlabels = NULL,
                             .indent_mods = NULL,
                             na_str = "",
                             .section_div = NA_character_) {
  if (multivar && control$interaction) {
    warning(paste(
      "Interactions are not available for multivariate cox regression using summarize_coxreg.",
      "The model will be calculated without interaction effects."
    ))
  }
  if (control$interaction && !"arm" %in% names(variables)) {
    stop("To include interactions please specify 'arm' in variables.")
  }

  .stats <- if (!"arm" %in% names(variables) || multivar) { # only valid statistics
    intersect(c("hr", "ci", "pval"), .stats)
  } else if (control$interaction) {
    intersect(c("n", "hr", "ci", "pval", "pval_inter"), .stats)
  } else {
    intersect(c("n", "hr", "ci", "pval"), .stats)
  }
  stat_labels <- c(
    n = "n", hr = "Hazard Ratio", ci = paste0(control$conf_level * 100, "% CI"),
    pval = "p-value", pval_inter = "Interaction p-value"
  )
  stat_labels <- stat_labels[names(stat_labels) %in% .stats]
  .formats <- .formats[names(.formats) %in% .stats]
  env <- new.env() # create caching environment

  lyt <- lyt %>%
    split_cols_by_multivar(
      vars = rep(common_var, length(.stats)),
      varlabels = stat_labels,
      extra_args = list(
        .stats = .stats, .formats = .formats, .indent_mods = .indent_mods, na_str = rep(na_str, length(.stats)),
        cache_env = replicate(length(.stats), list(env))
      )
    )

  if ("arm" %in% names(variables)) { # treatment effect
    lyt <- lyt %>%
      split_rows_by(
        common_var,
        split_label = "Treatment:",
        label_pos = "visible",
        child_labels = "hidden",
        section_div = head(.section_div, 1)
      )
    if (!multivar) {
      lyt <- lyt %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar,
            labelstr = ""
          )
        )
    } else { # treatment level effects
      lyt <- lyt %>%
        summarize_row_groups(
          cfun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar
          )
        ) %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(eff = TRUE, control = control, variables = variables, multivar = multivar, labelstr = "")
        )
    }
  }

  if ("covariates" %in% names(variables)) { # covariate main effects
    lyt <- lyt %>%
      split_rows_by_multivar(
        vars = variables$covariates,
        varlabels = varlabels,
        split_label = "Covariate:",
        nested = FALSE,
        child_labels = if (multivar || control$interaction || !"arm" %in% names(variables)) "default" else "hidden",
        section_div = tail(.section_div, 1)
      )
    if (multivar || control$interaction || !"arm" %in% names(variables)) {
      lyt <- lyt %>%
        summarize_row_groups(
          cfun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, at = at, control = control, multivar = multivar,
            var_main = if (multivar) multivar else control$interaction
          )
        )
    } else {
      if (!is.null(varlabels)) names(varlabels) <- variables$covariates
      lyt <- lyt %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, at = at, control = control, multivar = multivar,
            var_main = if (multivar) multivar else control$interaction,
            labelstr = if (is.null(varlabels)) "" else varlabels
          )
        )
    }

    if (!"arm" %in% names(variables)) control$interaction <- TRUE # special case: univar no arm
    if (multivar || control$interaction) { # covariate level effects
      lyt <- lyt %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(variables = variables, at = at, control = control, multivar = multivar, labelstr = ""),
          indent_mod = if (!"arm" %in% names(variables) || multivar) 0L else -1L
        )
    }
  }

  lyt
}

#' Occurrence Table Pruning
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Family of constructor and condition functions to flexibly prune occurrence tables.
#' The condition functions always return whether the row result is higher than the threshold.
#' Since they are of class [CombinationFunction()] they can be logically combined with other condition
#' functions.
#'
#' @note Since most table specifications are worded positively, we name our constructor and condition
#'   functions positively, too. However, note that the result of [keep_rows()] says what
#'   should be pruned, to conform with the [rtables::prune_table()] interface.
#'
#' @examples
#' \donttest{
#' tab <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("RACE") %>%
#'   split_rows_by("STRATA1") %>%
#'   summarize_row_groups() %>%
#'   analyze_vars("COUNTRY", .stats = "count_fraction") %>%
#'   build_table(DM)
#' }
#'
#' @name prune_occurrences
NULL

#' @describeIn prune_occurrences Constructor for creating pruning functions based on
#'   a row condition function. This removes all analysis rows (`TableRow`) that should be
#'   pruned, i.e., don't fulfill the row condition. It removes the sub-tree if there are no
#'   children left.
#'
#' @param row_condition (`CombinationFunction`)\cr condition function which works on individual
#'   analysis rows and flags whether these should be kept in the pruned table.
#'
#' @return
#' * `keep_rows()` returns a pruning function that can be used with [rtables::prune_table()]
#'   to prune an `rtables` table.
#'
#' @examples
#' \donttest{
#' # `keep_rows`
#' is_non_empty <- !CombinationFunction(all_zero_or_na)
#' prune_table(tab, keep_rows(is_non_empty))
#' }
#'
#' @export
keep_rows <- function(row_condition) {
  checkmate::assert_function(row_condition)
  function(table_tree) {
    if (inherits(table_tree, "TableRow")) {
      return(!row_condition(table_tree))
    }
    children <- tree_children(table_tree)
    identical(length(children), 0L)
  }
}

#' @describeIn prune_occurrences Constructor for creating pruning functions based on
#'   a condition for the (first) content row in leaf tables. This removes all leaf tables where
#'   the first content row does not fulfill the condition. It does not check individual rows.
#'   It then proceeds recursively by removing the sub tree if there are no children left.
#'
#' @param content_row_condition (`CombinationFunction`)\cr condition function which works on individual
#'   first content rows of leaf tables and flags whether these leaf tables should be kept in the pruned table.
#'
#' @return
#' * `keep_content_rows()` returns a pruning function that checks the condition on the first content
#'   row of leaf tables in the table.
#'
#' @examples
#' # `keep_content_rows`
#' \donttest{
#' more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab))
#' prune_table(tab, keep_content_rows(more_than_twenty))
#' }
#'
#' @export
keep_content_rows <- function(content_row_condition) {
  checkmate::assert_function(content_row_condition)
  function(table_tree) {
    if (is_leaf_table(table_tree)) {
      content_row <- h_content_first_row(table_tree)
      return(!content_row_condition(content_row))
    }
    if (inherits(table_tree, "DataRow")) {
      return(FALSE)
    }
    children <- tree_children(table_tree)
    identical(length(children), 0L)
  }
}

#' @describeIn prune_occurrences Constructor for creating condition functions on total counts in the specified columns.
#'
#' @param atleast (`count` or `proportion`)\cr threshold which should be met in order to keep the row.
#' @param ... arguments for row or column access, see [`rtables_access`]: either `col_names` (`character`) including
#'   the names of the columns which should be used, or alternatively `col_indices` (`integer`) giving the indices
#'   directly instead.
#'
#' @return
#' * `has_count_in_cols()` returns a condition function that sums the counts in the specified column.
#'
#' @examples
#' \donttest{
#' more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_one))
#' }
#'
#' @export
has_count_in_cols <- function(atleast, ...) {
  checkmate::assert_count(atleast)
  CombinationFunction(function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    total_count <- sum(row_counts)
    total_count >= atleast
  })
}

#' @describeIn prune_occurrences Constructor for creating condition functions on any of the counts in
#'   the specified columns satisfying a threshold.
#'
#' @param atleast (`count` or `proportion`)\cr threshold which should be met in order to keep the row.
#'
#' @return
#' * `has_count_in_any_col()` returns a condition function that compares the counts in the
#'   specified columns with the threshold.
#'
#' @examples
#' \donttest{
#' # `has_count_in_any_col`
#' any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab))
#' prune_table(tab, keep_rows(any_more_than_one))
#' }
#'
#' @export
has_count_in_any_col <- function(atleast, ...) {
  checkmate::assert_count(atleast)
  CombinationFunction(function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    any(row_counts >= atleast)
  })
}

#' @describeIn prune_occurrences Constructor for creating condition functions on total fraction in
#'   the specified columns.
#'
#' @return
#' * `has_fraction_in_cols()` returns a condition function that sums the counts in the
#'   specified column, and computes the fraction by dividing by the total column counts.
#'
#' @examples
#' \donttest{
#' # `has_fraction_in_cols`
#' more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_five_percent))
#' }
#'
#' @export
has_fraction_in_cols <- function(atleast, ...) {
  assert_proportion_value(atleast, include_boundaries = TRUE)
  CombinationFunction(function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    total_count <- sum(row_counts)
    col_counts <- h_col_counts(table_row, ...)
    total_n <- sum(col_counts)
    total_percent <- total_count / total_n
    total_percent >= atleast
  })
}

#' @describeIn prune_occurrences Constructor for creating condition functions on any fraction in
#'   the specified columns.
#'
#' @return
#' * `has_fraction_in_any_col()` returns a condition function that looks at the fractions
#'  in the specified columns and checks whether any of them fulfill the threshold.
#'
#' @examples
#' \donttest{
#' # `has_fraction_in_any_col`
#' any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_five_percent))
#' }
#'
#' @export
has_fraction_in_any_col <- function(atleast, ...) {
  assert_proportion_value(atleast, include_boundaries = TRUE)
  CombinationFunction(function(table_row) {
    row_fractions <- h_row_fractions(table_row, ...)
    any(row_fractions >= atleast)
  })
}

#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
#'   between the fractions reported in each specified column.
#'
#' @return
#' * `has_fractions_difference()` returns a condition function that extracts the fractions of each
#'   specified column, and computes the difference of the minimum and maximum.
#'
#' @examples
#' \donttest{
#' # `has_fractions_difference`
#' more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_five_percent_diff))
#' }
#'
#' @export
has_fractions_difference <- function(atleast, ...) {
  assert_proportion_value(atleast, include_boundaries = TRUE)
  CombinationFunction(function(table_row) {
    fractions <- h_row_fractions(table_row, ...)
    difference <- diff(range(fractions))
    difference >= atleast
  })
}

#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
#'   between the counts reported in each specified column.
#'
#' @return
#' * `has_counts_difference()` returns a condition function that extracts the counts of each
#'   specified column, and computes the difference of the minimum and maximum.
#'
#' @examples
#' \donttest{
#' more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_one_diff))
#' }
#'
#' @export
has_counts_difference <- function(atleast, ...) {
  checkmate::assert_count(atleast)
  CombinationFunction(function(table_row) {
    counts <- h_row_counts(table_row, ...)
    difference <- diff(range(counts))
    difference >= atleast
  })
}

#' Helper Functions for Tabulating Binary Response by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions that tabulate in a data frame statistics such as response rate
#' and odds ratio for population subgroups.
#'
#' @inheritParams argument_convention
#' @inheritParams response_subgroups
#' @param arm (`factor`)\cr the treatment group variable.
#'
#' @details Main functionality is to prepare data for use in a layout-creating function.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
#'   droplevels() %>%
#'   mutate(
#'     # Reorder levels of factor to make the placebo group the reference arm.
#'     ARM = fct_relevel(ARM, "B: Placebo"),
#'     rsp = AVALC == "CR"
#'   )
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' @name h_response_subgroups
NULL

#' @describeIn h_response_subgroups helper to prepare a data frame of binary responses by arm.
#'
#' @return
#' * `h_proportion_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, and `prop`.
#'
#' @examples
#' h_proportion_df(
#'   c(TRUE, FALSE, FALSE),
#'   arm = factor(c("A", "A", "B"), levels = c("A", "B"))
#' )
#'
#' @export
h_proportion_df <- function(rsp, arm) {
  checkmate::assert_logical(rsp)
  assert_valid_factor(arm, len = length(rsp))
  non_missing_rsp <- !is.na(rsp)
  rsp <- rsp[non_missing_rsp]
  arm <- arm[non_missing_rsp]

  lst_rsp <- split(rsp, arm)
  lst_results <- Map(function(x, arm) {
    if (length(x) > 0) {
      s_prop <- s_proportion(df = x)
      data.frame(
        arm = arm,
        n = length(x),
        n_rsp = unname(s_prop$n_prop[1]),
        prop = unname(s_prop$n_prop[2]),
        stringsAsFactors = FALSE
      )
    } else {
      data.frame(
        arm = arm,
        n = 0L,
        n_rsp = NA,
        prop = NA,
        stringsAsFactors = FALSE
      )
    }
  }, lst_rsp, names(lst_rsp))

  df <- do.call(rbind, args = c(lst_results, make.row.names = FALSE))
  df$arm <- factor(df$arm, levels = levels(arm))
  df
}

#' @describeIn h_response_subgroups summarizes proportion of binary responses by arm and across subgroups
#'    in a data frame. `variables` corresponds to the names of variables found in `data`, passed as a named list and
#'    requires elements `rsp`, `arm` and optionally `subgroups`. `groups_lists` optionally specifies
#'    groupings for `subgroups` variables.
#'
#' @return
#' * `h_proportion_subgroups_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`,
#'   `var`, `var_label`, and `row_type`.
#'
#' @examples
#' h_proportion_subgroups_df(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f
#' )
#'
#' # Define groupings for BMRKR2 levels.
#' h_proportion_subgroups_df(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' @export
h_proportion_subgroups_df <- function(variables,
                                      data,
                                      groups_lists = list(),
                                      label_all = "All Patients") {
  checkmate::assert_character(variables$rsp)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  assert_df_with_variables(data, variables)
  checkmate::assert_string(label_all)

  # Add All Patients.
  result_all <- h_proportion_df(data[[variables$rsp]], data[[variables$arm]])
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  # Add Subgroups.
  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)

    l_result <- lapply(l_data, function(grp) {
      result <- h_proportion_df(grp$df[[variables$rsp]], grp$df[[variables$arm]])
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"

    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' @describeIn h_response_subgroups helper to prepare a data frame with estimates of
#'   the odds ratio between a treatment and a control arm.
#'
#' @inheritParams response_subgroups
#' @param strata_data (`factor`, `data.frame` or `NULL`)\cr required if stratified analysis is performed.
#'
#' @return
#' * `h_odds_ratio_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`, and
#'   optionally `pval` and `pval_label`.
#'
#' @examples
#' # Unstratatified analysis.
#' h_odds_ratio_df(
#'   c(TRUE, FALSE, FALSE, TRUE),
#'   arm = factor(c("A", "A", "B", "B"), levels = c("A", "B"))
#' )
#'
#' # Include p-value.
#' h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = "chisq")
#'
#' # Stratatified analysis.
#' h_odds_ratio_df(
#'   rsp = adrs_f$rsp,
#'   arm = adrs_f$ARM,
#'   strata_data = adrs_f[, c("STRATA1", "STRATA2")],
#'   method = "cmh"
#' )
#'
#' @export
h_odds_ratio_df <- function(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL) {
  assert_valid_factor(arm, n.levels = 2, len = length(rsp))

  df_rsp <- data.frame(
    rsp = rsp,
    arm = arm
  )

  if (!is.null(strata_data)) {
    strata_var <- interaction(strata_data, drop = TRUE)
    strata_name <- "strata"

    assert_valid_factor(strata_var, len = nrow(df_rsp))

    df_rsp[[strata_name]] <- strata_var
  } else {
    strata_name <- NULL
  }

  l_df <- split(df_rsp, arm)

  if (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) > 0) {
    # Odds ratio and CI.
    result_odds_ratio <- s_odds_ratio(
      df = l_df[[2]],
      .var = "rsp",
      .ref_group = l_df[[1]],
      .in_ref_col = FALSE,
      .df_row = df_rsp,
      variables = list(arm = "arm", strata = strata_name),
      conf_level = conf_level
    )

    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = unname(result_odds_ratio$n_tot["n_tot"]),
      or = unname(result_odds_ratio$or_ci["est"]),
      lcl = unname(result_odds_ratio$or_ci["lcl"]),
      ucl = unname(result_odds_ratio$or_ci["ucl"]),
      conf_level = conf_level,
      stringsAsFactors = FALSE
    )

    if (!is.null(method)) {
      # Test for difference.
      result_test <- s_test_proportion_diff(
        df = l_df[[2]],
        .var = "rsp",
        .ref_group = l_df[[1]],
        .in_ref_col = FALSE,
        variables = list(strata = strata_name),
        method = method
      )

      df$pval <- as.numeric(result_test$pval)
      df$pval_label <- obj_label(result_test$pval)
    }

    # In those cases cannot go through the model so will obtain n_tot from data.
  } else if (
    (nrow(l_df[[1]]) == 0 && nrow(l_df[[2]]) > 0) ||
      (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) == 0)
  ) {
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = sum(stats::complete.cases(df_rsp)),
      or = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      stringsAsFactors = FALSE
    )
    if (!is.null(method)) {
      df$pval <- NA
      df$pval_label <- NA
    }
  } else {
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = 0L,
      or = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      stringsAsFactors = FALSE
    )

    if (!is.null(method)) {
      df$pval <- NA
      df$pval_label <- NA
    }
  }

  df
}

#' @describeIn h_response_subgroups summarizes estimates of the odds ratio between a treatment and a control
#'   arm across subgroups in a data frame. `variables` corresponds to the names of variables found in
#'   `data`, passed as a named list and requires elements `rsp`, `arm` and optionally `subgroups`
#'   and `strata`. `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @return
#' * `h_odds_ratio_subgroups_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`,
#'   `conf_level`, `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @examples
#' # Unstratified analysis.
#' h_odds_ratio_subgroups_df(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f
#' )
#'
#' # Stratified analysis.
#' h_odds_ratio_subgroups_df(
#'   variables = list(
#'     rsp = "rsp",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2"),
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adrs_f
#' )
#'
#' # Define groupings of BMRKR2 levels.
#' h_odds_ratio_subgroups_df(
#'   variables = list(
#'     rsp = "rsp",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' @export
h_odds_ratio_subgroups_df <- function(variables,
                                      data,
                                      groups_lists = list(),
                                      conf_level = 0.95,
                                      method = NULL,
                                      label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_odds_ratio_subgroups_df() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  checkmate::assert_character(variables$rsp)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_character(variables$strata, null.ok = TRUE)
  assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  assert_df_with_variables(data, variables)
  checkmate::assert_string(label_all)

  strata_data <- if (is.null(variables$strata)) {
    NULL
  } else {
    data[, variables$strata, drop = FALSE]
  }

  # Add All Patients.
  result_all <- h_odds_ratio_df(
    rsp = data[[variables$rsp]],
    arm = data[[variables$arm]],
    strata_data = strata_data,
    conf_level = conf_level,
    method = method
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)

    l_result <- lapply(l_data, function(grp) {
      grp_strata_data <- if (is.null(variables$strata)) {
        NULL
      } else {
        grp$df[, variables$strata, drop = FALSE]
      }

      result <- h_odds_ratio_df(
        rsp = grp$df[[variables$rsp]],
        arm = grp$df[[variables$arm]],
        strata_data = grp_strata_data,
        conf_level = conf_level,
        method = method
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })

    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"

    rbind(
      result_all,
      result_subgroups
    )
  }
}

# Utility functions to cooperate with {rtables} package

#' Convert Table into Matrix of Strings
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to use mostly within tests. `with_spaces`parameter allows
#' to test not only for content but also indentation and table structure.
#' `print_txt_to_copy` instead facilitate the testing development by returning a well
#' formatted text that needs only to be copied and pasted in the expected output.
#'
#' @inheritParams formatters::toString
#' @param x `rtables` table.
#' @param with_spaces (`logical`)\cr should the tested table keep the indentation and other relevant spaces?
#' @param print_txt_to_copy  (`logical`)\cr utility to have a way to copy the input table directly
#'   into the expected variable instead of copying it too manually.
#'
#' @return A `matrix` of `string`s. If `print_txt_to_copy = TRUE` the well formatted printout of the
#'   table will be printed to console, ready to be copied as a expected value.
#'
#' @examples
#' tbl <- basic_table() %>%
#'   split_rows_by("SEX") %>%
#'   split_cols_by("ARM") %>%
#'   analyze("AGE") %>%
#'   build_table(tern_ex_adsl)
#'
#' to_string_matrix(tbl, widths = ceiling(propose_column_widths(tbl) / 2))
#'
#' @export
to_string_matrix <- function(x, widths = NULL, max_width = NULL,
                             hsep = formatters::default_hsep(),
                             with_spaces = TRUE, print_txt_to_copy = FALSE) {
  checkmate::assert_flag(with_spaces)
  checkmate::assert_flag(print_txt_to_copy)
  checkmate::assert_int(max_width, null.ok = TRUE)

  if (inherits(x, "MatrixPrintForm")) {
    tx <- x
  } else {
    tx <- matrix_form(x, TRUE)
  }

  tf_wrap <- FALSE
  if (!is.null(max_width)) {
    tf_wrap <- TRUE
  }

  # Producing the matrix to test
  if (with_spaces) {
    out <- strsplit(toString(tx, widths = widths, tf_wrap = tf_wrap, max_width = max_width, hsep = hsep), "\\n")[[1]]
  } else {
    out <- tx$strings
  }

  # Printing to console formatted output that needs to be copied in "expected"
  if (print_txt_to_copy) {
    out_tmp <- out
    if (!with_spaces) {
      out_tmp <- apply(out, 1, paste0, collapse = '", "')
    }
    cat(paste0('c(\n  "', paste0(out_tmp, collapse = '",\n  "'), '"\n)'))
  }

  # Return values
  return(out)
}

#' Blank for Missing Input
#'
#' Helper function to use in tabulating model results.
#'
#' @param x (`vector`)\cr input for a cell.
#'
#' @return An empty `character` vector if all entries in `x` are missing (`NA`), otherwise
#'   the unlisted version of `x`.
#'
#' @keywords internal
unlist_and_blank_na <- function(x) {
  unl <- unlist(x)
  if (all(is.na(unl))) {
    character()
  } else {
    unl
  }
}

#' Constructor for Content Functions given Data Frame with Flag Input
#'
#' This can be useful for tabulating model results.
#'
#' @param analysis_var (`string`)\cr variable name for the column containing values to be returned by the
#'   content function.
#' @param flag_var (`string`)\cr variable name for the logical column identifying which row should be returned.
#' @param format (`string`)\cr `rtables` format to use.
#'
#' @return A content function which gives `df$analysis_var` at the row identified by
#'   `.df_row$flag` in the given format.
#'
#' @keywords internal
cfun_by_flag <- function(analysis_var,
                         flag_var,
                         format = "xx",
                         .indent_mods = NULL) {
  checkmate::assert_string(analysis_var)
  checkmate::assert_string(flag_var)
  function(df, labelstr) {
    row_index <- which(df[[flag_var]])
    x <- unlist_and_blank_na(df[[analysis_var]][row_index])
    formatters::with_label(
      rcell(x, format = format, indent_mod = .indent_mods),
      labelstr
    )
  }
}

#' Content Row Function to Add Row Total to Labels
#'
#' This takes the label of the latest row split level and adds the row total from `df` in parentheses.
#' This function differs from [c_label_n_alt()] by taking row counts from `df` rather than
#' `alt_counts_df`, and is used by [add_rowcounts()] when `alt_counts` is set to `FALSE`.
#'
#' @inheritParams argument_convention
#'
#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.
#'
#' @note It is important here to not use `df` but rather `.N_row` in the implementation, because
#'   the former is already split by columns and will refer to the first column of the data only.
#'
#' @seealso [c_label_n_alt()] which performs the same function but retrieves row counts from
#'   `alt_counts_df` instead of `df`.
#'
#' @keywords internal
c_label_n <- function(df,
                      labelstr,
                      .N_row) { # nolint
  label <- paste0(labelstr, " (N=", .N_row, ")")
  in_rows(
    .list = list(row_count = formatters::with_label(c(.N_row, .N_row), label)),
    .formats = c(row_count = function(x, ...) "")
  )
}

#' Content Row Function to Add `alt_counts_df` Row Total to Labels
#'
#' This takes the label of the latest row split level and adds the row total from `alt_counts_df`
#' in parentheses. This function differs from [c_label_n()] by taking row counts from `alt_counts_df`
#' rather than `df`, and is used by [add_rowcounts()] when `alt_counts` is set to `TRUE`.
#'
#' @inheritParams argument_convention
#'
#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.
#'
#' @seealso [c_label_n()] which performs the same function but retrieves row counts from `df` instead
#'   of `alt_counts_df`.
#'
#' @keywords internal
c_label_n_alt <- function(df,
                          labelstr,
                          .alt_df_row) {
  N_row_alt <- nrow(.alt_df_row) # nolint
  label <- paste0(labelstr, " (N=", N_row_alt, ")")
  in_rows(
    .list = list(row_count = formatters::with_label(c(N_row_alt, N_row_alt), label)),
    .formats = c(row_count = function(x, ...) "")
  )
}

#' Layout Creating Function to Add Row Total Counts
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This works analogously to [rtables::add_colcounts()] but on the rows. This function
#'  is a wrapper for [rtables::summarize_row_groups()].
#'
#' @inheritParams argument_convention
#' @param alt_counts (`flag`)\cr whether row counts should be taken from `alt_counts_df` (`TRUE`)
#'   or from `df` (`FALSE`). Defaults to `FALSE`.
#'
#' @return A modified layout where the latest row split labels now have the row-wise
#'   total counts (i.e. without column-based subsetting) attached in parentheses.
#'
#' @note Row count values are contained in these row count rows but are not displayed
#'   so that they are not considered zero rows by default when pruning.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   split_rows_by("RACE", split_fun = drop_split_levels) %>%
#'   add_rowcounts() %>%
#'   analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx") %>%
#'   build_table(DM)
#'
#' @export
add_rowcounts <- function(lyt, alt_counts = FALSE) {
  summarize_row_groups(
    lyt,
    cfun = if (alt_counts) c_label_n_alt else c_label_n
  )
}

#' Obtain Column Indices
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to extract column indices from a `VTableTree` for a given
#' vector of column names.
#'
#' @param table_tree (`VTableTree`)\cr table to extract the indices from.
#' @param col_names (`character`)\cr vector of column names.
#'
#' @return A vector of column indices.
#'
#' @export
h_col_indices <- function(table_tree, col_names) {
  checkmate::assert_class(table_tree, "VTableNodeInfo")
  checkmate::assert_subset(col_names, names(attr(col_info(table_tree), "cextra_args")), empty.ok = FALSE)
  match(col_names, names(attr(col_info(table_tree), "cextra_args")))
}

#' Labels or Names of List Elements
#'
#' Internal helper function for working with nested statistic function results which typically
#' don't have labels but names that we can use.
#'
#' @param x a list.
#'
#' @return A `character` vector with the labels or names for the list elements.
#'
#' @keywords internal
labels_or_names <- function(x) {
  checkmate::assert_multi_class(x, c("data.frame", "list"))
  labs <- sapply(x, obj_label)
  nams <- rlang::names2(x)
  label_is_null <- sapply(labs, is.null)
  result <- unlist(ifelse(label_is_null, nams, labs))
  return(result)
}

#' Convert to `rtable`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a new generic function to convert objects to `rtable` tables.
#'
#' @param x the object which should be converted to an `rtable`.
#' @param ... additional arguments for methods.
#'
#' @return An `rtables` table object. Note that the concrete class will depend on the method used.
#'
#' @export
as.rtable <- function(x, ...) { # nolint
  UseMethod("as.rtable", x)
}

#' @describeIn as.rtable method for converting `data.frame` that contain numeric columns to `rtable`.
#'
#' @param format the format which should be used for the columns.
#'
#' @method as.rtable data.frame
#'
#' @examples
#' x <- data.frame(
#'   a = 1:10,
#'   b = rnorm(10)
#' )
#' as.rtable(x)
#'
#' @export
as.rtable.data.frame <- function(x, format = "xx.xx", ...) {
  checkmate::assert_numeric(unlist(x))
  do.call(
    rtable,
    c(
      list(
        header = labels_or_names(x),
        format = format
      ),
      Map(
        function(row, row_name) {
          do.call(
            rrow,
            c(as.list(unname(row)),
              row.name = row_name
            )
          )
        },
        row = as.data.frame(t(x)),
        row_name = rownames(x)
      )
    )
  )
}

#' Split parameters
#'
#' @description `r lifecycle::badge("stable")`
#'
#' It divides the data in the vector `param` into the groups defined by `f` based on specified `values`. It is relevant
#' in `rtables` layers so as to distribute parameters `.stats` or' `.formats` into lists with items corresponding to
#' specific analysis function.
#'
#' @param param (`vector`)\cr the parameter to be split.
#' @param value (`vector`)\cr the value used to split.
#' @param f (`list` of `vectors`)\cr the reference to make the split
#'
#' @return A named `list` with the same element names as `f`, each containing the elements specified in `.stats`.
#'
#' @examples
#' f <- list(
#'   surv = c("pt_at_risk", "event_free_rate", "rate_se", "rate_ci"),
#'   surv_diff = c("rate_diff", "rate_diff_ci", "ztest_pval")
#' )
#'
#' .stats <- c("pt_at_risk", "rate_diff")
#' h_split_param(.stats, .stats, f = f)
#'
#' # $surv
#' # [1] "pt_at_risk"
#' #
#' # $surv_diff
#' # [1] "rate_diff"
#'
#' .formats <- c("pt_at_risk" = "xx", "event_free_rate" = "xxx")
#' h_split_param(.formats, names(.formats), f = f)
#'
#' # $surv
#' # pt_at_risk event_free_rate
#' # "xx"           "xxx"
#' #
#' # $surv_diff
#' # NULL
#'
#' @export
h_split_param <- function(param,
                          value,
                          f) {
  y <- lapply(f, function(x) param[value %in% x])
  lapply(y, function(x) if (length(x) == 0) NULL else x)
}

#' Get Selected Statistics Names
#'
#' Helper function to be used for creating `afun`.
#'
#' @param .stats (`vector` or `NULL`)\cr input to the layout creating function. Note that `NULL` means
#'   in this context that all default statistics should be used.
#' @param all_stats (`character`)\cr all statistics which can be selected here potentially.
#'
#' @return A `character` vector with the selected statistics.
#'
#' @keywords internal
afun_selected_stats <- function(.stats, all_stats) {
  checkmate::assert_character(.stats, null.ok = TRUE)
  checkmate::assert_character(all_stats)
  if (is.null(.stats)) {
    all_stats
  } else {
    intersect(.stats, all_stats)
  }
}

#' Add Variable Labels to Top Left Corner in Table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper layout creating function to just append the variable labels of a given variables vector
#' from a given dataset in the top left corner. If a variable label is not found then the
#' variable name itself is used instead. Multiple variable labels are concatenated with slashes.
#'
#' @inheritParams argument_convention
#' @param vars (`character`)\cr variable names of which the labels are to be looked up in `df`.
#' @param indent (`integer`)\cr non-negative number of nested indent space, default to 0L which means no indent.
#'   1L means two spaces indent, 2L means four spaces indent and so on.
#'
#' @return A modified layout with the new variable label(s) added to the top-left material.
#'
#' @note This is not an optimal implementation of course, since we are using here the data set
#'   itself during the layout creation. When we have a more mature `rtables` implementation then
#'   this will also be improved or not necessary anymore.
#'
#' @examples
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   split_rows_by("SEX") %>%
#'   append_varlabels(DM, "SEX") %>%
#'   analyze("AGE", afun = mean) %>%
#'   append_varlabels(DM, "AGE", indent = 1)
#' build_table(lyt, DM)
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("SEX") %>%
#'   analyze("AGE", afun = mean) %>%
#'   append_varlabels(DM, c("SEX", "AGE"))
#' build_table(lyt, DM)
#'
#' @export
append_varlabels <- function(lyt, df, vars, indent = 0L) {
  if (checkmate::test_flag(indent)) {
    warning("indent argument is now accepting integers. Boolean indent will be converted to integers.")
    indent <- as.integer(indent)
  }

  checkmate::assert_data_frame(df)
  checkmate::assert_character(vars)
  checkmate::assert_count(indent)

  lab <- formatters::var_labels(df[vars], fill = TRUE)
  lab <- paste(lab, collapse = " / ")
  space <- paste(rep(" ", indent * 2), collapse = "")
  lab <- paste0(space, lab)

  append_topleft(lyt, lab)
}

#' Default string replacement for `NA` values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The default string used to represent `NA` values. This value is used as the default
#' value for the `na_str` argument throughout the `tern` package, and printed in place
#' of `NA` values in output tables. If not specified for each `tern` function by the user
#' via the `na_str` argument, or in the R environment options via [set_default_na_str()],
#' then `NA` is used.
#'
#' @param na_str (`string`)\cr Single string value to set in the R environment options as
#'   the default value to replace `NA`s. Use `getOption("tern_default_na_str")` to check the
#'   current value set in the R environment (defaults to `NULL` if not set).
#'
#' @name default_na_str
NULL

#' @describeIn default_na_str Getter for default `NA` value replacement string.
#'
#' @return
#' * `default_na_str` returns the current value if an R environment option has been set
#'   for `"tern_default_na_str"`, or `NA_character_` otherwise.
#'
#' @examples
#' # Default settings
#' default_na_str()
#' getOption("tern_default_na_str")
#'
#' # Set custom value
#' set_default_na_str("<Missing>")
#'
#' # Settings after value has been set
#' default_na_str()
#' getOption("tern_default_na_str")
#'
#' @export
default_na_str <- function() {
  getOption("tern_default_na_str", default = NA_character_)
}

#' @describeIn default_na_str Setter for default `NA` value replacement string. Sets the
#'   option `"tern_default_na_str"` within the R environment.
#'
#' @return
#' * `set_default_na_str` has no return value.
#'
#' @export
set_default_na_str <- function(na_str) {
  checkmate::assert_character(na_str, len = 1, null.ok = TRUE)
  options("tern_default_na_str" = na_str)
}

#' Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This function is useful to label grid grobs (also `ggplot2`, and `lattice` plots)
#' with title, footnote, and page numbers.
#'
#' @inheritParams grid::grob
#' @param grob a grid grob object, optionally `NULL` if only a `grob` with the decoration should be shown.
#' @param titles vector of character strings. Vector elements are separated by a newline and strings are wrapped
#'   according to the page width.
#' @param footnotes vector of character string. Same rules as for `titles`.
#' @param page string with page numeration, if `NULL` then no page number is displayed.
#' @param width_titles unit object
#' @param width_footnotes unit object
#' @param border boolean, whether a a border should be drawn around the plot or not.
#' @param margins unit object of length 4
#' @param padding  unit object of length 4
#' @param outer_margins  unit object of length 4
#' @param gp_titles a `gpar` object
#' @param gp_footnotes a `gpar` object
#'
#' @return A grid grob (`gTree`).
#'
#' @details The titles and footnotes will be ragged, i.e. each title will be wrapped individually.
#'
#' @examples
#' library(grid)
#'
#' titles <- c(
#'   "Edgar Anderson's Iris Data",
#'   paste(
#'     "This famous (Fisher's or Anderson's) iris data set gives the measurements",
#'     "in centimeters of the variables sepal length and width and petal length",
#'     "and width, respectively, for 50 flowers from each of 3 species of iris."
#'   )
#' )
#'
#' footnotes <- c(
#'   "The species are Iris setosa, versicolor, and virginica.",
#'   paste(
#'     "iris is a data frame with 150 cases (rows) and 5 variables (columns) named",
#'     "Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species."
#'   )
#' )
#'
#' ## empty plot
#' grid.newpage()
#'
#' grid.draw(
#'   decorate_grob(
#'     NULL,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 4 of 10"
#'   )
#' )
#'
#' # grid
#' p <- gTree(
#'   children = gList(
#'     rectGrob(),
#'     xaxisGrob(),
#'     yaxisGrob(),
#'     textGrob("Sepal.Length", y = unit(-4, "lines")),
#'     textGrob("Petal.Length", x = unit(-3.5, "lines"), rot = 90),
#'     pointsGrob(iris$Sepal.Length, iris$Petal.Length, gp = gpar(col = iris$Species), pch = 16)
#'   ),
#'   vp = vpStack(plotViewport(), dataViewport(xData = iris$Sepal.Length, yData = iris$Petal.Length))
#' )
#' grid.newpage()
#' grid.draw(p)
#'
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     grob = p,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 6 of 129"
#'   )
#' )
#'
#' ## with ggplot2
#' library(ggplot2)
#'
#' p_gg <- ggplot2::ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) +
#'   ggplot2::geom_point()
#' p_gg
#' p <- ggplotGrob(p_gg)
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     grob = p,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 6 of 129"
#'   )
#' )
#'
#' ## with lattice
#' library(lattice)
#'
#' xyplot(Sepal.Length ~ Petal.Length, data = iris, col = iris$Species)
#' p <- grid.grab()
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     grob = p,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 6 of 129"
#'   )
#' )
#'
#' # with gridExtra - no borders
#' library(gridExtra)
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     tableGrob(
#'       head(mtcars)
#'     ),
#'     titles = "title",
#'     footnotes = "footnote",
#'     border = FALSE
#'   )
#' )
#'
#' @export
decorate_grob <- function(grob,
                          titles,
                          footnotes,
                          page = "",
                          width_titles = grid::unit(1, "npc") - grid::unit(1.5, "cm"),
                          width_footnotes = grid::unit(1, "npc") - grid::unit(1.5, "cm"),
                          border = TRUE,
                          margins = grid::unit(c(1, 0, 1, 0), "lines"),
                          padding = grid::unit(rep(1, 4), "lines"),
                          outer_margins = grid::unit(c(2, 1.5, 3, 1.5), "cm"),
                          gp_titles = grid::gpar(),
                          gp_footnotes = grid::gpar(fontsize = 8),
                          name = NULL,
                          gp = grid::gpar(),
                          vp = NULL) {
  st_titles <- split_text_grob(
    titles,
    x = 0, y = 1,
    just = c("left", "top"),
    width = width_titles,
    vp = grid::viewport(layout.pos.row = 1, layout.pos.col = 1),
    gp = gp_titles
  )

  st_footnotes <- split_text_grob(
    footnotes,
    x = 0, y = 1,
    just = c("left", "top"),
    width = width_footnotes,
    vp = grid::viewport(layout.pos.row = 3, layout.pos.col = 1),
    gp = gp_footnotes
  )

  pg_footnote <- grid::textGrob(
    paste("\n", page),
    x = 1, y = 0,
    just = c("right", "bottom"),
    vp = grid::viewport(layout.pos.row = 4, layout.pos.col = 1),
    gp = gp_footnotes
  )

  grid::gTree(
    grob = grob,
    titles = titles,
    footnotes = footnotes,
    page = page,
    width_titles = width_titles,
    width_footnotes = width_footnotes,
    border = border,
    margins = margins,
    padding = padding,
    outer_margins = outer_margins,
    gp_titles = gp_titles,
    gp_footnotes = gp_footnotes,
    children = grid::gList(
      grid::gTree(
        children = grid::gList(
          st_titles,
          grid::gTree(
            children = grid::gList(
              if (border) grid::rectGrob(),
              grid::gTree(
                children = grid::gList(
                  grob
                ),
                vp = grid::plotViewport(margins = padding)
              )
            ),
            vp = grid::vpStack(
              grid::viewport(layout.pos.row = 2, layout.pos.col = 1),
              grid::plotViewport(margins = margins)
            )
          ),
          st_footnotes,
          pg_footnote
        ),
        childrenvp = NULL,
        name = "titles_grob_footnotes",
        vp = grid::vpStack(
          grid::plotViewport(margins = outer_margins),
          grid::viewport(
            layout = grid::grid.layout(
              nrow = 4, ncol = 1,
              heights = grid::unit.c(
                grid::grobHeight(st_titles),
                grid::unit(1, "null"),
                grid::grobHeight(st_footnotes),
                grid::grobHeight(pg_footnote)
              )
            )
          )
        )
      )
    ),
    name = name,
    gp = gp,
    vp = vp,
    cl = "decoratedGrob"
  )
}

#' @importFrom grid validDetails
#' @noRd
validDetails.decoratedGrob <- function(x) {
  checkmate::assert_character(x$titles)
  checkmate::assert_character(x$footnotes)

  if (!is.null(x$grob)) {
    checkmate::assert_true(grid::is.grob(x$grob))
  }
  if (length(x$page) == 1) {
    checkmate::assert_character(x$page)
  }
  if (!grid::is.unit(x$outer_margins)) {
    checkmate::assert_vector(x$outer_margins, len = 4)
  }
  if (!grid::is.unit(x$margins)) {
    checkmate::assert_vector(x$margins, len = 4)
  }
  if (!grid::is.unit(x$padding)) {
    checkmate::assert_vector(x$padding, len = 4)
  }

  x
}

#' @importFrom grid widthDetails
#' @noRd
widthDetails.decoratedGrob <- function(x) {
  grid::unit(1, "null")
}

#' @importFrom grid heightDetails
#' @noRd
heightDetails.decoratedGrob <- function(x) {
  grid::unit(1, "null")
}

# Adapted from Paul Murell R Graphics 2nd Edition
# https://www.stat.auckland.ac.nz/~paul/RG2e/interactgrid-splittext.R
split_string <- function(text, width) {
  strings <- strsplit(text, " ")
  out_string <- NA
  for (string_i in seq_along(strings)) {
    newline_str <- strings[[string_i]]
    if (length(newline_str) == 0) newline_str <- ""
    if (is.na(out_string[string_i])) {
      out_string[string_i] <- newline_str[[1]][[1]]
      linewidth <- grid::stringWidth(out_string[string_i])
    }
    gapwidth <- grid::stringWidth(" ")
    availwidth <- as.numeric(width)
    if (length(newline_str) > 1) {
      for (i in seq(2, length(newline_str))) {
        width_i <- grid::stringWidth(newline_str[i])
        if (grid::convertWidth(linewidth + gapwidth + width_i, grid::unitType(width), valueOnly = TRUE) < availwidth) {
          sep <- " "
          linewidth <- linewidth + gapwidth + width_i
        } else {
          sep <- "\n"
          linewidth <- width_i
        }
        out_string[string_i] <- paste(out_string[string_i], newline_str[i], sep = sep)
      }
    }
  }
  paste(out_string, collapse = "\n")
}

#' Split Text According To Available Text Width
#'
#' Dynamically wrap text.
#'
#' @inheritParams grid::grid.text
#' @param text character string
#' @param width a unit object specifying max width of text
#'
#' @return A text grob.
#'
#' @details This code is taken from `R Graphics by Paul Murell, 2nd edition`
#'
#' @keywords internal
split_text_grob <- function(text,
                            x = grid::unit(0.5, "npc"),
                            y = grid::unit(0.5, "npc"),
                            width = grid::unit(1, "npc"),
                            just = "centre",
                            hjust = NULL,
                            vjust = NULL,
                            default.units = "npc", # nolint
                            name = NULL,
                            gp = grid::gpar(),
                            vp = NULL) {
  if (!grid::is.unit(x)) x <- grid::unit(x, default.units)
  if (!grid::is.unit(y)) y <- grid::unit(y, default.units)
  if (!grid::is.unit(width)) width <- grid::unit(width, default.units)
  if (grid::unitType(x) %in% c("sum", "min", "max")) x <- grid::convertUnit(x, default.units)
  if (grid::unitType(y) %in% c("sum", "min", "max")) y <- grid::convertUnit(y, default.units)
  if (grid::unitType(width) %in% c("sum", "min", "max")) width <- grid::convertUnit(width, default.units)

  if (length(gp) > 0) { # account for effect of gp on text width
    width <- width * grid::convertWidth(grid::grobWidth(grid::textGrob(text)), "npc", valueOnly = TRUE) /
      grid::convertWidth(grid::grobWidth(grid::textGrob(text, gp = gp)), "npc", valueOnly = TRUE)
  }

  ## if it is a fixed unit then we do not need to recalculate when viewport resized
  if (!inherits(width, "unit.arithmetic") && !is.null(attr(width, "unit")) &&
    attr(width, "unit") %in% c("cm", "inches", "mm", "points", "picas", "bigpts", "dida", "cicero", "scaledpts")) { # nolint
    attr(text, "fixed_text") <- paste(vapply(text, split_string, character(1), width = width), collapse = "\n")
  }

  grid::grid.text(
    label = split_string(text, width),
    x = x, y = y,
    just = just,
    hjust = hjust,
    vjust = vjust,
    rot = 0,
    check.overlap = FALSE,
    name = name,
    gp = gp,
    vp = vp,
    draw = FALSE
  )
}

#' @importFrom grid validDetails
#' @noRd
validDetails.dynamicSplitText <- function(x) {
  checkmate::assert_character(x$text)
  checkmate::assert_true(grid::is.unit(x$width))
  checkmate::assert_vector(x$width, len = 1)
  x
}

#' @importFrom grid heightDetails
#' @noRd
heightDetails.dynamicSplitText <- function(x) {
  txt <- if (!is.null(attr(x$text, "fixed_text"))) {
    attr(x$text, "fixed_text")
  } else {
    paste(vapply(x$text, split_string, character(1), width = x$width), collapse = "\n")
  }
  grid::stringHeight(txt)
}

#' @importFrom grid widthDetails
#' @noRd
widthDetails.dynamicSplitText <- function(x) {
  x$width
}

#' @importFrom grid drawDetails
#' @noRd
drawDetails.dynamicSplitText <- function(x, recording) {
  txt <- if (!is.null(attr(x$text, "fixed_text"))) {
    attr(x$text, "fixed_text")
  } else {
    paste(vapply(x$text, split_string, character(1), width = x$width), collapse = "\n")
  }

  x$width <- NULL
  x$label <- txt
  x$text <- NULL
  class(x) <- c("text", class(x)[-1])

  grid::grid.draw(x)
}

#' Update Page Number
#'
#' Automatically updates page number.
#'
#' @param npages number of pages in total
#' @param ... passed on to [decorate_grob()]
#'
#' @return Closure that increments the page number.
#'
#' @keywords internal
decorate_grob_factory <- function(npages, ...) {
  current_page <- 0
  function(grob) {
    current_page <<- current_page + 1
    if (current_page > npages) {
      stop(paste("current page is", current_page, "but max.", npages, "specified."))
    }
    decorate_grob(grob = grob, page = paste("Page", current_page, "of", npages), ...)
  }
}

#' Decorate Set of `grobs` and Add Page Numbering
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Note that this uses the [decorate_grob_factory()] function.
#'
#' @param grobs a list of grid grobs
#' @param ... arguments passed on to [decorate_grob()].
#'
#' @return A decorated grob.
#'
#' @examples
#' library(ggplot2)
#' library(grid)
#' g <- with(data = iris, {
#'   list(
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Length, Sepal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Length, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Length, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Petal.Length, Petal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     )
#'   )
#' })
#' lg <- decorate_grob_set(grobs = g, titles = "Hello\nOne\nTwo\nThree", footnotes = "")
#'
#' draw_grob(lg[[1]])
#' draw_grob(lg[[2]])
#' draw_grob(lg[[6]])
#'
#' @export
decorate_grob_set <- function(grobs, ...) {
  n <- length(grobs)
  lgf <- decorate_grob_factory(npages = n, ...)
  lapply(grobs, lgf)
}

#' Helper Function to create a new `SMQ` variable in `ADAE` by stacking `SMQ` and/or `CQ` records.
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper Function to create a new `SMQ` variable in `ADAE` that consists of all adverse events belonging to
#' selected Standardized/Customized queries. The new dataset will only contain records of the adverse events
#' belonging to any of the selected baskets. Remember that `na_str` must match the needed pre-processing
#' done with [df_explicit_na()] to have the desired output.
#'
#' @inheritParams argument_convention
#' @param baskets (`character`)\cr variable names of the selected Standardized/Customized queries.
#' @param smq_varlabel (`string`)\cr a label for the new variable created.
#' @param keys (`character`)\cr names of the key variables to be returned along with the new variable created.
#' @param aag_summary (`data.frame`)\cr containing the `SMQ` baskets and the levels of interest for the final `SMQ`
#'   variable. This is useful when there are some levels of interest that are not observed in the `df` dataset.
#'   The two columns of this dataset should be named `basket` and `basket_name`.
#'
#' @return `data.frame` with variables in `keys` taken from `df` and new variable `SMQ` containing
#'   records belonging to the baskets selected via the `baskets` argument.
#'
#' @examples
#' adae <- tern_ex_adae[1:20, ] %>% df_explicit_na()
#' h_stack_by_baskets(df = adae)
#'
#' aag <- data.frame(
#'   NAMVAR = c("CQ01NAM", "CQ02NAM", "SMQ01NAM", "SMQ02NAM"),
#'   REFNAME = c(
#'     "D.2.1.5.3/A.1.1.1.1 aesi", "X.9.9.9.9/Y.8.8.8.8 aesi",
#'     "C.1.1.1.3/B.2.2.3.1 aesi", "C.1.1.1.3/B.3.3.3.3 aesi"
#'   ),
#'   SCOPE = c("", "", "BROAD", "BROAD"),
#'   stringsAsFactors = FALSE
#' )
#'
#' basket_name <- character(nrow(aag))
#' cq_pos <- grep("^(CQ).+NAM$", aag$NAMVAR)
#' smq_pos <- grep("^(SMQ).+NAM$", aag$NAMVAR)
#' basket_name[cq_pos] <- aag$REFNAME[cq_pos]
#' basket_name[smq_pos] <- paste0(
#'   aag$REFNAME[smq_pos], "(", aag$SCOPE[smq_pos], ")"
#' )
#'
#' aag_summary <- data.frame(
#'   basket = aag$NAMVAR,
#'   basket_name = basket_name,
#'   stringsAsFactors = TRUE
#' )
#'
#' result <- h_stack_by_baskets(df = adae, aag_summary = aag_summary)
#' all(levels(aag_summary$basket_name) %in% levels(result$SMQ))
#'
#' h_stack_by_baskets(
#'   df = adae,
#'   aag_summary = NULL,
#'   keys = c("STUDYID", "USUBJID", "AEDECOD", "ARM"),
#'   baskets = "SMQ01NAM"
#' )
#'
#' @export
h_stack_by_baskets <- function(df,
                               baskets = grep("^(SMQ|CQ).+NAM$", names(df), value = TRUE),
                               smq_varlabel = "Standardized MedDRA Query",
                               keys = c("STUDYID", "USUBJID", "ASTDTM", "AEDECOD", "AESEQ"),
                               aag_summary = NULL,
                               na_str = "<Missing>") {
  smq_nam <- baskets[startsWith(baskets, "SMQ")]
  # SC corresponding to NAM
  smq_sc <- gsub(pattern = "NAM", replacement = "SC", x = smq_nam, fixed = TRUE)
  smq <- stats::setNames(smq_sc, smq_nam)

  checkmate::assert_character(baskets)
  checkmate::assert_string(smq_varlabel)
  checkmate::assert_data_frame(df)
  checkmate::assert_true(all(startsWith(baskets, "SMQ") | startsWith(baskets, "CQ")))
  checkmate::assert_true(all(endsWith(baskets, "NAM")))
  checkmate::assert_subset(baskets, names(df))
  checkmate::assert_subset(keys, names(df))
  checkmate::assert_subset(smq_sc, names(df))
  checkmate::assert_string(na_str)

  if (!is.null(aag_summary)) {
    assert_df_with_variables(
      df = aag_summary,
      variables = list(val = c("basket", "basket_name"))
    )
    # Warning in case there is no match between `aag_summary$basket` and `baskets` argument.
    # Honestly, I think those should completely match. Target baskets should be the same.
    if (length(intersect(baskets, unique(aag_summary$basket))) == 0) {
      warning("There are 0 baskets in common between aag_summary$basket and `baskets` argument.")
    }
  }

  var_labels <- c(formatters::var_labels(df[, keys]), "SMQ" = smq_varlabel)

  # convert `na_str` records from baskets to NA for the later loop and from wide to long steps
  df[, c(baskets, smq_sc)][df[, c(baskets, smq_sc)] == na_str] <- NA

  if (all(is.na(df[, baskets]))) { # in case there is no level for the target baskets
    df_long <- df[-seq_len(nrow(df)), keys] # we just need an empty dataframe keeping all factor levels
  } else {
    # Concatenate SMQxxxNAM with corresponding SMQxxxSC
    df_cnct <- df[, c(keys, baskets[startsWith(baskets, "CQ")])]

    for (nam in names(smq)) {
      sc <- smq[nam] # SMQxxxSC corresponding to SMQxxxNAM
      nam_notna <- !is.na(df[[nam]])
      new_colname <- paste(nam, sc, sep = "_")
      df_cnct[nam_notna, new_colname] <- paste0(df[[nam]], "(", df[[sc]], ")")[nam_notna]
    }

    df_cnct$unique_id <- seq(1, nrow(df_cnct))
    var_cols <- names(df_cnct)[!(names(df_cnct) %in% c(keys, "unique_id"))]
    # have to convert df_cnct from tibble to dataframe
    # as it throws a warning otherwise about rownames.
    # tibble do not support rownames and reshape creates rownames

    df_long <- stats::reshape(
      data = as.data.frame(df_cnct),
      varying = var_cols,
      v.names = "SMQ",
      idvar = names(df_cnct)[names(df_cnct) %in% c(keys, "unique_id")],
      direction = "long",
      new.row.names = seq(prod(length(var_cols), nrow(df_cnct)))
    )

    df_long <- df_long[!is.na(df_long[, "SMQ"]), !(names(df_long) %in% c("time", "unique_id"))]
    df_long$SMQ <- as.factor(df_long$SMQ)
  }

  smq_levels <- setdiff(levels(df_long[["SMQ"]]), na_str)

  if (!is.null(aag_summary)) {
    # A warning in case there is no match between df and aag_summary records
    if (length(intersect(smq_levels, unique(aag_summary$basket_name))) == 0) {
      warning("There are 0 basket levels in common between aag_summary$basket_name and df.")
    }
    df_long[["SMQ"]] <- factor(
      df_long[["SMQ"]],
      levels = sort(
        c(
          smq_levels,
          setdiff(unique(aag_summary$basket_name), smq_levels)
        )
      )
    )
  } else {
    all_na_basket_flag <- vapply(df[, baskets], function(x) {
      all(is.na(x))
    }, FUN.VALUE = logical(1))
    all_na_basket <- baskets[all_na_basket_flag]

    df_long[["SMQ"]] <- factor(
      df_long[["SMQ"]],
      levels = sort(c(smq_levels, all_na_basket))
    )
  }
  formatters::var_labels(df_long) <- var_labels
  tibble::tibble(df_long)
}

#' Re-implemented [range()] Default S3 method for numerical objects
#'
#' This function returns `c(NA, NA)` instead of `c(-Inf, Inf)` for zero-length data
#' without any warnings.
#'
#' @param x (`numeric`)\cr a sequence of numbers for which the range is computed.
#' @param na.rm (`logical`)\cr indicating if `NA` should be omitted.
#' @param finite (`logical`)\cr indicating if non-finite elements should be removed.
#'
#' @return A 2-element vector of class `numeric`.
#'
#' @keywords internal
range_noinf <- function(x, na.rm = FALSE, finite = FALSE) { # nolint

  checkmate::assert_numeric(x)

  if (finite) {
    x <- x[is.finite(x)] # removes NAs too
  } else if (na.rm) {
    x <- x[!is.na(x)]
  }

  if (length(x) == 0) {
    rval <- c(NA, NA)
    mode(rval) <- typeof(x)
  } else {
    rval <- c(min(x, na.rm = FALSE), max(x, na.rm = FALSE))
  }

  return(rval)
}

#' Utility function to create label for confidence interval
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#'
#' @return A `string`.
#'
#' @export
f_conf_level <- function(conf_level) {
  assert_proportion_value(conf_level)
  paste0(conf_level * 100, "% CI")
}

#' Utility function to create label for p-value
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param test_mean (`number`)\cr mean value to test under the null hypothesis.
#'
#' @return A `string`.
#'
#' @export
f_pval <- function(test_mean) {
  checkmate::assert_numeric(test_mean, len = 1)
  paste0("p-value (H0: mean = ", test_mean, ")")
}

#' Utility function to return a named list of covariate names.
#'
#' @param covariates (`character`)\cr a vector that can contain single variable names (such as
#'   `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
#'
#' @return A named `list` of `character` vector.
#'
#' @keywords internal
get_covariates <- function(covariates) {
  checkmate::assert_character(covariates)
  cov_vars <- unique(trimws(unlist(strsplit(covariates, "\\*"))))
  stats::setNames(as.list(cov_vars), cov_vars)
}

#' Replicate Entries of a Vector if Required
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Replicate entries of a vector if required.
#'
#' @inheritParams argument_convention
#' @param n (`count`)\cr how many entries we need.
#'
#' @return `x` if it has the required length already or is `NULL`,
#'   otherwise if it is scalar the replicated version of it with `n` entries.
#'
#' @note This function will fail if `x` is not of length `n` and/or is not a scalar.
#'
#' @export
to_n <- function(x, n) {
  if (is.null(x)) {
    NULL
  } else if (length(x) == 1) {
    rep(x, n)
  } else if (length(x) == n) {
    x
  } else {
    stop("dimension mismatch")
  }
}

#' Check Element Dimension
#'
#' Checks if the elements in `...` have the same dimension.
#'
#' @param ... (`data.frame`s or `vector`s)\cr any data frames/vectors.
#' @param omit_null (`logical`)\cr whether `NULL` elements in `...` should be omitted from the check.
#'
#' @return A `logical` value.
#'
#' @keywords internal
check_same_n <- function(..., omit_null = TRUE) {
  dots <- list(...)

  n_list <- Map(
    function(x, name) {
      if (is.null(x)) {
        if (omit_null) {
          NA_integer_
        } else {
          stop("arg", name, "is not supposed to be NULL")
        }
      } else if (is.data.frame(x)) {
        nrow(x)
      } else if (is.atomic(x)) {
        length(x)
      } else {
        stop("data structure for ", name, "is currently not supported")
      }
    },
    dots, names(dots)
  )

  n <- stats::na.omit(unlist(n_list))

  if (length(unique(n)) > 1) {
    sel <- which(n != n[1])
    stop("Dimension mismatch:", paste(names(n)[sel], collapse = ", "), " do not have N=", n[1])
  }

  TRUE
}

#' Utility function to check if a float is equal to another float
#'
#' @description Uses `.Machine$double.eps` as the tolerance for the comparison.
#'
#' @param x (`float`)\cr A single number.
#' @param y (`float`)\cr A single number.
#'
#' @return `TRUE`, if identical. `FALSE`, otherwise
#'
#' @keywords internal
.is_equal_float <- function(x, y) {
  checkmate::assert_number(x)
  checkmate::assert_number(y)

  # Define a tolerance
  tolerance <- .Machine$double.eps

  # Check if x is close enough to y
  abs(x - y) < tolerance
}

#' Make Names Without Dots
#'
#' @param nams (`character`)\cr vector of original names.
#'
#' @return A `character` `vector` of proper names, which does not use dots in contrast to [make.names()].
#'
#' @keywords internal
make_names <- function(nams) {
  orig <- make.names(nams)
  gsub(".", "", x = orig, fixed = TRUE)
}

#' Conversion of Months to Days
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Conversion of Months to Days. This is an approximative calculation because it
#' considers each month as having an average of 30.4375 days.
#'
#' @param x (`numeric`)\cr time in months.
#'
#' @return A `numeric` vector with the time in days.
#'
#' @examples
#' x <- c(13.25, 8.15, 1, 2.834)
#' month2day(x)
#'
#' @export
month2day <- function(x) {
  checkmate::assert_numeric(x)
  x * 30.4375
}

#' Conversion of Days to Months
#'
#' @param x (`numeric`)\cr time in days.
#'
#' @return A `numeric` vector with the time in months.
#'
#' @examples
#' x <- c(403, 248, 30, 86)
#' day2month(x)
#'
#' @export
day2month <- function(x) {
  checkmate::assert_numeric(x)
  x / 30.4375
}

#' Return an empty numeric if all elements are `NA`.
#'
#' @param x (`numeric`)\cr vector.
#'
#' @return An empty `numeric` if all elements of `x` are `NA`, otherwise `x`.
#'
#' @examples
#' x <- c(NA, NA, NA)
#' # Internal function - empty_vector_if_na
#' @keywords internal
empty_vector_if_na <- function(x) {
  if (all(is.na(x))) {
    numeric()
  } else {
    x
  }
}

#' Combine Two Vectors Element Wise
#'
#' @param x (`vector`)\cr first vector to combine.
#' @param y (`vector`)\cr second vector to combine.
#'
#' @return A `list` where each element combines corresponding elements of `x` and `y`.
#'
#' @examples
#' combine_vectors(1:3, 4:6)
#'
#' @export
combine_vectors <- function(x, y) {
  checkmate::assert_vector(x)
  checkmate::assert_vector(y, len = length(x))

  result <- lapply(as.data.frame(rbind(x, y)), `c`)
  names(result) <- NULL
  result
}

#' Extract Elements by Name
#'
#' This utility function extracts elements from a vector `x` by `names`.
#' Differences to the standard `[` function are:
#'
#' - If `x` is `NULL`, then still always `NULL` is returned (same as in base function).
#' - If `x` is not `NULL`, then the intersection of its names is made with `names` and those
#'   elements are returned. That is, `names` which don't appear in `x` are not returned as `NA`s.
#'
#' @param x (named `vector`)\cr where to extract named elements from.
#' @param names (`character`)\cr vector of names to extract.
#'
#' @return `NULL` if `x` is `NULL`, otherwise the extracted elements from `x`.
#'
#' @keywords internal
extract_by_name <- function(x, names) {
  if (is.null(x)) {
    return(NULL)
  }
  checkmate::assert_named(x)
  checkmate::assert_character(names)
  which_extract <- intersect(names(x), names)
  if (length(which_extract) > 0) {
    x[which_extract]
  } else {
    NULL
  }
}

#' Labels for Adverse Event Baskets
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param aesi (`character`)\cr with standardized `MedDRA` query name (e.g. `SMQzzNAM`) or customized query
#'   name (e.g. `CQzzNAM`).
#' @param scope (`character`)\cr with scope of query (e.g. `SMQzzSC`).
#'
#' @return A `string` with the standard label for the `AE` basket.
#'
#' @examples
#' adae <- tern_ex_adae
#'
#' # Standardized query label includes scope.
#' aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC)
#'
#' # Customized query label.
#' aesi_label(adae$CQ01NAM)
#'
#' @export
aesi_label <- function(aesi, scope = NULL) {
  checkmate::assert_character(aesi)
  checkmate::assert_character(scope, null.ok = TRUE)
  aesi_label <- obj_label(aesi)
  aesi <- sas_na(aesi)
  aesi <- unique(aesi)[!is.na(unique(aesi))]

  lbl <- if (length(aesi) == 1 && !is.null(scope)) {
    scope <- sas_na(scope)
    scope <- unique(scope)[!is.na(unique(scope))]
    checkmate::assert_string(scope)
    paste0(aesi, " (", scope, ")")
  } else if (length(aesi) == 1 && is.null(scope)) {
    aesi
  } else {
    aesi_label
  }

  lbl
}

#' Indicate Study Arm Variable in Formula
#'
#' We use `study_arm` to indicate the study arm variable in `tern` formulas.
#'
#' @param x arm information
#'
#' @return `x`
#'
#' @keywords internal
study_arm <- function(x) {
  structure(x, varname = deparse(substitute(x)))
}

#' Smooth Function with Optional Grouping
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This produces `loess` smoothed estimates of `y` with Student confidence intervals.
#'
#' @param df (`data.frame`)\cr data set containing all analysis variables.
#' @param x (`character`)\cr value with x column name.
#' @param y (`character`)\cr value with y column name.
#' @param groups (`character`)\cr vector with optional grouping variables names.
#' @param level (`numeric`)\cr level of confidence interval to use (0.95 by default).
#'
#' @return A `data.frame` with original `x`, smoothed `y`, `ylow`, and `yhigh`, and
#'   optional `groups` variables formatted as `factor` type.
#'
#' @export
get_smooths <- function(df, x, y, groups = NULL, level = 0.95) {
  checkmate::assert_data_frame(df)
  df_cols <- colnames(df)
  checkmate::assert_string(x)
  checkmate::assert_subset(x, df_cols)
  checkmate::assert_numeric(df[[x]])
  checkmate::assert_string(y)
  checkmate::assert_subset(y, df_cols)
  checkmate::assert_numeric(df[[y]])

  if (!is.null(groups)) {
    checkmate::assert_character(groups)
    checkmate::assert_subset(groups, df_cols)
  }

  smooths <- function(x, y) {
    stats::predict(stats::loess(y ~ x), se = TRUE)
  }

  if (!is.null(groups)) {
    cc <- stats::complete.cases(df[c(x, y, groups)])
    df_c <- df[cc, c(x, y, groups)]
    df_c_ordered <- df_c[do.call("order", as.list(df_c[, groups, drop = FALSE])), , drop = FALSE]
    df_c_g <- data.frame(Map(as.factor, df_c_ordered[groups]))

    df_smooth_raw <-
      by(df_c_ordered, df_c_g, function(d) {
        plx <- smooths(d[[x]], d[[y]])
        data.frame(
          x = d[[x]],
          y = plx$fit,
          ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
          yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
        )
      })

    df_smooth <- do.call(rbind, df_smooth_raw)
    df_smooth[groups] <- df_c_g

    df_smooth
  } else {
    cc <- stats::complete.cases(df[c(x, y)])
    df_c <- df[cc, ]
    plx <- smooths(df_c[[x]], df_c[[y]])

    df_smooth <- data.frame(
      x = df_c[[x]],
      y = plx$fit,
      ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
      yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
    )

    df_smooth
  }
}

#' Number of Available (Non-Missing Entries) in a Vector
#'
#' Small utility function for better readability.
#'
#' @param x (`any`)\cr vector in which to count non-missing values.
#'
#' @return Number of non-missing values.
#'
#' @keywords internal
n_available <- function(x) {
  sum(!is.na(x))
}

#' Reapply Variable Labels
#'
#' This is a helper function that is used in tests.
#'
#' @param x (`vector`)\cr vector of elements that needs new labels.
#' @param varlabels (`character`)\cr vector of labels for `x`.
#' @param ... further parameters to be added to the list.
#'
#' @return `x` with variable labels reapplied.
#'
#' @export
reapply_varlabels <- function(x, varlabels, ...) {
  named_labels <- c(as.list(varlabels), list(...))
  formatters::var_labels(x)[names(named_labels)] <- as.character(named_labels)
  x
}

# Wrapper function of survival::clogit so that when model fitting failed, a more useful message would show
clogit_with_tryCatch <- function(formula, data, ...) { # nolint
  tryCatch(
    survival::clogit(formula = formula, data = data, ...),
    error = function(e) stop("model not built successfully with survival::clogit")
  )
}

#' Difference Test for Two Proportions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Various tests were implemented to test the difference between two proportions.
#'
#' @inheritParams argument_convention
#' @param method (`string`)\cr one of `chisq`, `cmh`, `fisher`, or `schouten`; specifies the test used
#'   to calculate the p-value.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("test_proportion_diff")`
#'   to see available statistics for this function.
#'
#' @seealso [h_prop_diff_test]
#'
#' @name prop_diff_test
#' @order 1
NULL

#' @describeIn prop_diff_test Statistics function which tests the difference between two proportions.
#'
#' @return
#' * `s_test_proportion_diff()` returns a named `list` with a single item `pval` with an attribute `label`
#'   describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same.
#'
#' @keywords internal
s_test_proportion_diff <- function(df,
                                   .var,
                                   .ref_group,
                                   .in_ref_col,
                                   variables = list(strata = NULL),
                                   method = c("chisq", "schouten", "fisher", "cmh")) {
  method <- match.arg(method)
  y <- list(pval = "")

  if (!.in_ref_col) {
    assert_df_with_variables(df, list(rsp = .var))
    assert_df_with_variables(.ref_group, list(rsp = .var))
    rsp <- factor(
      c(.ref_group[[.var]], df[[.var]]),
      levels = c("TRUE", "FALSE")
    )
    grp <- factor(
      rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
      levels = c("ref", "Not-ref")
    )

    if (!is.null(variables$strata) || method == "cmh") {
      strata <- variables$strata
      checkmate::assert_false(is.null(strata))
      strata_vars <- stats::setNames(as.list(strata), strata)
      assert_df_with_variables(df, strata_vars)
      assert_df_with_variables(.ref_group, strata_vars)
      strata <- c(interaction(.ref_group[strata]), interaction(df[strata]))
    }

    tbl <- switch(method,
      cmh = table(grp, rsp, strata),
      table(grp, rsp)
    )

    y$pval <- switch(method,
      chisq = prop_chisq(tbl),
      cmh = prop_cmh(tbl),
      fisher = prop_fisher(tbl),
      schouten = prop_schouten(tbl)
    )
  }

  y$pval <- formatters::with_label(y$pval, d_test_proportion_diff(method))
  y
}

#' Description of the Difference Test Between Two Proportions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function that describes the analysis in `s_test_proportion_diff`.
#'
#' @inheritParams s_test_proportion_diff
#'
#' @return `string` describing the test from which the p-value is derived.
#'
#' @export
d_test_proportion_diff <- function(method) {
  checkmate::assert_string(method)
  meth_part <- switch(method,
    "schouten" = "Chi-Squared Test with Schouten Correction",
    "chisq" = "Chi-Squared Test",
    "cmh" = "Cochran-Mantel-Haenszel Test",
    "fisher" = "Fisher's Exact Test",
    stop(paste(method, "does not have a description"))
  )
  paste0("p-value (", meth_part, ")")
}

#' @describeIn prop_diff_test Formatted analysis function which is used as `afun` in `test_proportion_diff()`.
#'
#' @return
#' * `a_test_proportion_diff()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_test_proportion_diff <- make_afun(
  s_test_proportion_diff,
  .formats = c(pval = "x.xxxx | (<0.0001)"),
  .indent_mods = c(pval = 1L)
)

#' @describeIn prop_diff_test Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `test_proportion_diff()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_test_proportion_diff()` to the table layout.
#'
#' @examples
#' dta <- data.frame(
#'   rsp = sample(c(TRUE, FALSE), 100, TRUE),
#'   grp = factor(rep(c("A", "B"), each = 50)),
#'   strata = factor(rep(c("V", "W", "X", "Y", "Z"), each = 20))
#' )
#'
#' # With `rtables` pipelines.
#' l <- basic_table() %>%
#'   split_cols_by(var = "grp", ref_group = "B") %>%
#'   test_proportion_diff(
#'     vars = "rsp",
#'     method = "cmh", variables = list(strata = "strata")
#'   )
#'
#' build_table(l, df = dta)
#'
#' @export
#' @order 2
test_proportion_diff <- function(lyt,
                                 vars,
                                 variables = list(strata = NULL),
                                 method = c("chisq", "schouten", "fisher", "cmh"),
                                 na_str = default_na_str(),
                                 nested = TRUE,
                                 ...,
                                 var_labels = vars,
                                 show_labels = "hidden",
                                 table_names = vars,
                                 .stats = NULL,
                                 .formats = NULL,
                                 .labels = NULL,
                                 .indent_mods = NULL) {
  extra_args <- list(variables = variables, method = method, ...)

  afun <- make_afun(
    a_test_proportion_diff,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  analyze(
    lyt,
    vars,
    afun = afun,
    var_labels = var_labels,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Helper Functions to Test Proportion Differences
#'
#' Helper functions to implement various tests on the difference between two proportions.
#'
#' @param tbl (`matrix`)\cr matrix with two groups in rows and the binary response (`TRUE`/`FALSE`) in columns.
#'
#' @return A p-value.
#'
#' @seealso [prop_diff_test()] for implementation of these helper functions.
#'
#' @name h_prop_diff_test
NULL

#' @describeIn h_prop_diff_test performs Chi-Squared test. Internally calls [stats::prop.test()].
#'
#' @keywords internal
prop_chisq <- function(tbl) {
  checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
  tbl <- tbl[, c("TRUE", "FALSE")]
  if (any(colSums(tbl) == 0)) {
    return(1)
  }
  stats::prop.test(tbl, correct = FALSE)$p.value
}

#' @describeIn h_prop_diff_test performs stratified Cochran-Mantel-Haenszel test. Internally calls
#'   [stats::mantelhaen.test()]. Note that strata with less than two observations are automatically discarded.
#'
#' @param ary (`array`, 3 dimensions)\cr array with two groups in rows, the binary response
#'   (`TRUE`/`FALSE`) in columns, and the strata in the third dimension.
#'
#' @keywords internal
prop_cmh <- function(ary) {
  checkmate::assert_array(ary)
  checkmate::assert_integer(c(ncol(ary), nrow(ary)), lower = 2, upper = 2)
  checkmate::assert_integer(length(dim(ary)), lower = 3, upper = 3)
  strata_sizes <- apply(ary, MARGIN = 3, sum)
  if (any(strata_sizes < 5)) {
    warning("<5 data points in some strata. CMH test may be incorrect.")
    ary <- ary[, , strata_sizes > 1]
  }

  stats::mantelhaen.test(ary, correct = FALSE)$p.value
}

#' @describeIn h_prop_diff_test performs the Chi-Squared test with Schouten correction.
#'
#' @seealso Schouten correction is based upon \insertCite{Schouten1980-kd;textual}{tern}.
#'
#' @keywords internal
prop_schouten <- function(tbl) {
  checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
  tbl <- tbl[, c("TRUE", "FALSE")]
  if (any(colSums(tbl) == 0)) {
    return(1)
  }

  n <- sum(tbl)
  n1 <- sum(tbl[1, ])
  n2 <- sum(tbl[2, ])

  ad <- diag(tbl)
  bc <- diag(apply(tbl, 2, rev))
  ac <- tbl[, 1]
  bd <- tbl[, 2]

  t_schouten <- (n - 1) *
    (abs(prod(ad) - prod(bc)) - 0.5 * min(n1, n2))^2 /
    (n1 * n2 * sum(ac) * sum(bd))

  1 - stats::pchisq(t_schouten, df = 1)
}

#' @describeIn h_prop_diff_test performs the Fisher's exact test. Internally calls [stats::fisher.test()].
#'
#' @keywords internal
prop_fisher <- function(tbl) {
  checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
  tbl <- tbl[, c("TRUE", "FALSE")]
  stats::fisher.test(tbl)$p.value
}

#' Custom Split Functions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Collection of useful functions that are expanding on the core list of functions
#' provided by `rtables`. See [rtables::custom_split_funs] and [rtables::make_split_fun()]
#' for more information on how to make a custom split function. All these functions
#' work with [split_rows_by()] argument `split_fun` to modify the way the split
#' happens. For other split functions, consider consulting [`rtables::split_funcs`].
#'
#' @seealso [rtables::make_split_fun()]
#'
#' @name utils_split_funs
NULL

#' @describeIn utils_split_funs split function to place reference group facet at a specific position
#'  during post-processing stage.
#'
#' @param position (`string` or `integer`)\cr should it be `"first"` or `"last"` or in a specific position?
#'
#' @return
#' * `ref_group_position` returns an utility function that puts the reference group
#'  as first, last or at a certain position and needs to be assigned to `split_fun`.
#'
#' @examples
#' library(dplyr)
#'
#' dat <- data.frame(
#'   x = factor(letters[1:5], levels = letters[5:1]),
#'   y = 1:5
#' )
#'
#' # With rtables layout functions
#' basic_table() %>%
#'   split_cols_by("x", ref_group = "c", split_fun = ref_group_position("last")) %>%
#'   analyze("y") %>%
#'   build_table(dat)
#'
#' # With tern layout funcitons
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVAL = day2month(AVAL),
#'     is_event = CNSR == 0
#'   )
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position("first")) %>%
#'   add_colcounts() %>%
#'   surv_time(
#'     vars = "AVAL",
#'     var_labels = "Survival Time (Months)",
#'     is_event = "is_event",
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position(2)) %>%
#'   add_colcounts() %>%
#'   surv_time(
#'     vars = "AVAL",
#'     var_labels = "Survival Time (Months)",
#'     is_event = "is_event",
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
ref_group_position <- function(position = "first") {
  make_split_fun(
    post = list(
      function(splret, spl, fulldf) {
        if (!"ref_group_value" %in% methods::slotNames(spl)) {
          stop("Reference group is undefined.")
        }

        spl_var <- rtables:::spl_payload(spl)
        fulldf[[spl_var]] <- factor(fulldf[[spl_var]])
        init_lvls <- levels(fulldf[[spl_var]])

        if (!all(names(splret$values) %in% init_lvls)) {
          stop("This split function does not work with combination facets.")
        }

        ref_group_pos <- which(init_lvls == rtables:::spl_ref_group(spl))
        pos_choices <- c("first", "last")
        if (checkmate::test_choice(position, pos_choices) && position == "first") {
          pos <- 0
        } else if (checkmate::test_choice(position, pos_choices) && position == "last") {
          pos <- length(init_lvls)
        } else if (checkmate::test_int(position, lower = 1, upper = length(init_lvls))) {
          pos <- position - 1
        } else {
          stop("Wrong input for ref group position. It must be 'first', 'last', or a integer.")
        }

        reord_lvls <- append(init_lvls[-ref_group_pos], init_lvls[ref_group_pos], after = pos)
        ord <- match(reord_lvls, names(splret$values))

        make_split_result(
          splret$values[ord],
          splret$datasplit[ord],
          splret$labels[ord]
        )
      }
    )
  )
}

#' @describeIn utils_split_funs split function to change level order based on a `integer`
#'   vector or a `character` vector that represent the split variable's factor levels.
#'
#' @param order (`character` or `integer`)\cr vector of ordering indexes for the split facets.
#'
#' @return
#' * `level_order` returns an utility function that changes the original levels' order,
#'   depending on input `order` and split levels.
#'
#' @examples
#' # level_order --------
#' # Even if default would bring ref_group first, the original order puts it last
#' basic_table() %>%
#'   split_cols_by("Species", split_fun = level_order(c(1, 3, 2))) %>%
#'   analyze("Sepal.Length") %>%
#'   build_table(iris)
#'
#' # character vector
#' new_order <- level_order(levels(iris$Species)[c(1, 3, 2)])
#' basic_table() %>%
#'   split_cols_by("Species", ref_group = "virginica", split_fun = new_order) %>%
#'   analyze("Sepal.Length") %>%
#'   build_table(iris)
#'
#' @export
level_order <- function(order) {
  make_split_fun(
    post = list(
      function(splret, spl, fulldf) {
        if (checkmate::test_integerish(order)) {
          checkmate::assert_integerish(order, lower = 1, upper = length(splret$values))
          ord <- order
        } else {
          checkmate::assert_character(order, len = length(splret$values))
          checkmate::assert_set_equal(order, names(splret$values), ordered = FALSE)
          ord <- match(order, names(splret$values))
        }
        make_split_result(
          splret$values[ord],
          splret$datasplit[ord],
          splret$labels[ord]
        )
      }
    )
  )
}

#' Survival Time Analysis
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Summarize median survival time and CIs, percentiles of survival times, survival
#' time range of censored/event patients.
#'
#' @inheritParams argument_convention
#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function
#'   [control_surv_time()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for survival time.
#'   * `conf_type` (`string`)\cr confidence interval type. Options are "plain" (default), "log", or "log-log",
#'     see more in [survival::survfit()]. Note option "none" is not supported.
#'   * `quantiles` (`numeric`)\cr vector of length two to specify the quantiles of survival time.
#' @param ref_fn_censor (`flag`)\cr whether referential footnotes indicating censored observations should be printed
#'   when the `range` statistic is included.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("surv_time")`
#'   to see available statistics for this function.
#' @param .indent_mods (named `vector` of `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @examples
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVAL = day2month(AVAL),
#'     is_event = CNSR == 0
#'   )
#' df <- adtte_f %>% filter(ARMCD == "ARM A")
#'
#' @name survival_time
#' @order 1
NULL

#' @describeIn survival_time Statistics function which analyzes survival times.
#'
#' @return
#' * `s_surv_time()` returns the statistics:
#'   * `median`: Median survival time.
#'   * `median_ci`: Confidence interval for median time.
#'   * `quantiles`: Survival time for two specified quantiles.
#'   * `range_censor`: Survival time range for censored observations.
#'   * `range_event`: Survival time range for observations with events.
#'   * `range`: Survival time range for all observations.
#'
#' @keywords internal
s_surv_time <- function(df,
                        .var,
                        is_event,
                        control = control_surv_time()) {
  checkmate::assert_string(.var)
  assert_df_with_variables(df, list(tte = .var, is_event = is_event))
  checkmate::assert_numeric(df[[.var]], min.len = 1, any.missing = FALSE)
  checkmate::assert_logical(df[[is_event]], min.len = 1, any.missing = FALSE)

  conf_type <- control$conf_type
  conf_level <- control$conf_level
  quantiles <- control$quantiles

  formula <- stats::as.formula(paste0("survival::Surv(", .var, ", ", is_event, ") ~ 1"))
  srv_fit <- survival::survfit(
    formula = formula,
    data = df,
    conf.int = conf_level,
    conf.type = conf_type
  )
  srv_tab <- summary(srv_fit, extend = TRUE)$table
  srv_qt_tab <- stats::quantile(srv_fit, probs = quantiles)$quantile
  range_censor <- range_noinf(df[[.var]][!df[[is_event]]], na.rm = TRUE)
  range_event <- range_noinf(df[[.var]][df[[is_event]]], na.rm = TRUE)
  range <- range_noinf(df[[.var]], na.rm = TRUE)
  list(
    median = formatters::with_label(unname(srv_tab["median"]), "Median"),
    median_ci = formatters::with_label(
      unname(srv_tab[paste0(srv_fit$conf.int, c("LCL", "UCL"))]), f_conf_level(conf_level)
    ),
    quantiles = formatters::with_label(
      unname(srv_qt_tab), paste0(quantiles[1] * 100, "% and ", quantiles[2] * 100, "%-ile")
    ),
    range_censor = formatters::with_label(range_censor, "Range (censored)"),
    range_event = formatters::with_label(range_event, "Range (event)"),
    range = formatters::with_label(range, "Range")
  )
}

#' @describeIn survival_time Formatted analysis function which is used as `afun` in `surv_time()`.
#'
#' @return
#' * `a_surv_time()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_surv_time(
#'   df,
#'   .df_row = df,
#'   .var = "AVAL",
#'   is_event = "is_event"
#' )
#'
#' @export
a_surv_time <- function(df,
                        labelstr = "",
                        .var = NULL,
                        .df_row = NULL,
                        is_event,
                        control = control_surv_time(),
                        ref_fn_censor = TRUE,
                        .stats = NULL,
                        .formats = NULL,
                        .labels = NULL,
                        .indent_mods = NULL,
                        na_str = default_na_str()) {
  x_stats <- s_surv_time(
    df = df, .var = .var, is_event = is_event, control = control
  )
  rng_censor_lwr <- x_stats[["range_censor"]][1]
  rng_censor_upr <- x_stats[["range_censor"]][2]

  # Use method-specific defaults
  fmts <- c(median_ci = "(xx.x, xx.x)", quantiles = "xx.x, xx.x", range = "xx.x to xx.x")
  lbls <- c(median_ci = "95% CI", range = "Range", range_censor = "Range (censored)", range_event = "Range (event)")
  lbls_custom <- .labels
  .formats <- c(.formats, fmts[setdiff(names(fmts), names(.formats))])
  .labels <- c(.labels, lbls[setdiff(names(lbls), names(lbls_custom))])

  # Fill in with formatting defaults if needed
  .stats <- get_stats("surv_time", stats_in = .stats)
  .formats <- get_formats_from_stats(.stats, .formats)
  .labels <- get_labels_from_stats(.stats, .labels) %>% labels_use_control(control, lbls_custom)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods)

  x_stats <- x_stats[.stats]

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  cell_fns <- setNames(vector("list", length = length(x_stats)), .labels)
  if ("range" %in% names(x_stats) && ref_fn_censor) {
    if (x_stats[["range"]][1] == rng_censor_lwr && x_stats[["range"]][2] == rng_censor_upr) {
      cell_fns[[.labels[["range"]]]] <- "Censored observations: range minimum & maximum"
    } else if (x_stats[["range"]][1] == rng_censor_lwr) {
      cell_fns[[.labels[["range"]]]] <- "Censored observation: range minimum"
    } else if (x_stats[["range"]][2] == rng_censor_upr) {
      cell_fns[[.labels[["range"]]]] <- "Censored observation: range maximum"
    }
  }

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = .labels,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .format_na_strs = na_str,
    .cell_footnotes = cell_fns
  )
}

#' @describeIn survival_time Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `surv_time()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_surv_time()` to the table layout.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD") %>%
#'   add_colcounts() %>%
#'   surv_time(
#'     vars = "AVAL",
#'     var_labels = "Survival Time (Months)",
#'     is_event = "is_event",
#'     control = control_surv_time(conf_level = 0.9, conf_type = "log-log")
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
#' @order 2
surv_time <- function(lyt,
                      vars,
                      is_event,
                      control = control_surv_time(),
                      ref_fn_censor = TRUE,
                      na_str = default_na_str(),
                      nested = TRUE,
                      ...,
                      var_labels = "Time to Event",
                      show_labels = "visible",
                      table_names = vars,
                      .stats = c("median", "median_ci", "quantiles", "range"),
                      .formats = NULL,
                      .labels = NULL,
                      .indent_mods = c(median_ci = 1L)) {
  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str,
    is_event = is_event, control = control, ref_fn_censor = ref_fn_censor, ...
  )

  analyze(
    lyt = lyt,
    vars = vars,
    afun = a_surv_time,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Split Function to Configure Risk Difference Column
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Wrapper function for [rtables::add_combo_levels()] which configures settings for the risk difference
#' column to be added to an `rtables` object. To add a risk difference column to a table, this function
#' should be used as `split_fun` in calls to [rtables::split_cols_by()], followed by setting argument
#' `riskdiff` to `TRUE` in all following analyze function calls.
#'
#' @param arm_x (`character`)\cr Name of reference arm to use in risk difference calculations.
#' @param arm_y (`character`)\cr Names of one or more arms to compare to reference arm in risk difference
#'   calculations. A new column will be added for each value of `arm_y`.
#' @param col_label (`character`)\cr Labels to use when rendering the risk difference column within the table.
#'   If more than one comparison arm is specified in `arm_y`, default labels will specify which two arms are
#'   being compared (reference arm vs. comparison arm).
#' @param pct (`flag`)\cr whether output should be returned as percentages. Defaults to `TRUE`.
#'
#' @return A closure suitable for use as a split function (`split_fun`) within [rtables::split_cols_by()]
#'   when creating a table layout.
#'
#' @seealso [stat_propdiff_ci()] for details on risk difference calculation.
#'
#' @examples
#' adae <- tern_ex_adae
#' adae$AESEV <- factor(adae$AESEV)
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", split_fun = add_riskdiff(arm_x = "ARM A", arm_y = c("ARM B", "ARM C"))) %>%
#'   count_occurrences_by_grade(
#'     var = "AESEV",
#'     riskdiff = TRUE
#'   )
#'
#' tbl <- build_table(lyt, df = adae)
#' tbl
#'
#' @export
add_riskdiff <- function(arm_x,
                         arm_y,
                         col_label = paste0(
                           "Risk Difference (%) (95% CI)", if (length(arm_y) > 1) paste0("\n", arm_x, " vs. ", arm_y)
                         ),
                         pct = TRUE) {
  checkmate::assert_character(arm_x, len = 1)
  checkmate::assert_character(arm_y, min.len = 1)
  checkmate::assert_character(col_label, len = length(arm_y))

  combodf <- tibble::tribble(~valname, ~label, ~levelcombo, ~exargs)
  for (i in seq_len(length(arm_y))) {
    combodf <- rbind(
      combodf,
      tibble::tribble(
        ~valname, ~label, ~levelcombo, ~exargs,
        paste("riskdiff", arm_x, arm_y[i], sep = "_"), col_label[i], c(arm_x, arm_y[i]), list()
      )
    )
  }
  if (pct) combodf$valname <- paste0(combodf$valname, "_pct")
  add_combo_levels(combodf)
}

#' Analysis Function to Calculate Risk Difference Column Values
#'
#' In the risk difference column, this function uses the statistics function associated with `afun` to
#' calculates risk difference values from arm X (reference group) and arm Y. These arms are specified
#' when configuring the risk difference column which is done using the [add_riskdiff()] split function in
#' the previous call to [rtables::split_cols_by()]. For all other columns, applies `afun` as usual. This
#' function utilizes the [stat_propdiff_ci()] function to perform risk difference calculations.
#'
#' @inheritParams argument_convention
#' @param afun (named `list`)\cr A named list containing one name-value pair where the name corresponds to
#'   the name of the statistics function that should be used in calculations and the value is the corresponding
#'   analysis function.
#' @param s_args (named `list`)\cr Additional arguments to be passed to the statistics function and analysis
#'   function supplied in `afun`.
#'
#' @return A list of formatted [rtables::CellValue()].
#'
#' @seealso
#' * [stat_propdiff_ci()] for details on risk difference calculation.
#' * Split function [add_riskdiff()] which, when used as `split_fun` within [rtables::split_cols_by()] with
#'   `riskdiff` argument set to `TRUE` in subsequent analyze functions calls, adds a risk difference column
#'   to a table layout.
#'
#' @keywords internal
afun_riskdiff <- function(df,
                          labelstr = "",
                          .var,
                          .N_col, # nolint
                          .N_row, # nolint
                          .df_row,
                          .spl_context,
                          .all_col_counts,
                          .stats,
                          .formats = NULL,
                          .labels = NULL,
                          .indent_mods = NULL,
                          na_str = default_na_str(),
                          afun,
                          s_args = list()) {
  if (!any(grepl("riskdiff", names(.spl_context)))) {
    stop(
      "Please set up levels to use in risk difference calculations using the `add_riskdiff` ",
      "split function within `split_cols_by`. See ?add_riskdiff for details."
    )
  }
  checkmate::assert_list(afun, len = 1, types = "function")
  checkmate::assert_named(afun)
  afun_args <- list(
    .var = .var, .df_row = .df_row, .N_row = .N_row, denom = "N_col", labelstr = labelstr,
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  afun_args <- afun_args[intersect(names(afun_args), names(as.list(args(afun[[1]]))))]
  if ("denom" %in% names(s_args)) afun_args[["denom"]] <- NULL

  cur_split <- tail(.spl_context$cur_col_split_val[[1]], 1)
  if (!grepl("^riskdiff", cur_split)) {
    # Apply basic afun (no risk difference) in all other columns
    do.call(afun[[1]], args = c(list(df = df, .N_col = .N_col), afun_args, s_args))
  } else {
    arm_x <- strsplit(cur_split, "_")[[1]][2]
    arm_y <- strsplit(cur_split, "_")[[1]][3]
    if (length(.spl_context$cur_col_split[[1]]) > 1) { # Different split name for nested column splits
      arm_spl_x <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 2)], collapse = ""))
      arm_spl_y <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 3)], collapse = ""))
    } else {
      arm_spl_x <- arm_x
      arm_spl_y <- arm_y
    }
    N_col_x <- .all_col_counts[[arm_spl_x]] # nolint
    N_col_y <- .all_col_counts[[arm_spl_y]] # nolint
    cur_var <- tail(.spl_context$cur_col_split[[1]], 1)

    # Apply statistics function to arm X and arm Y data
    s_args <- c(s_args, afun_args[intersect(names(afun_args), names(as.list(args(names(afun)))))])
    s_x <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_x, ], .N_col = N_col_x), s_args))
    s_y <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_y, ], .N_col = N_col_y), s_args))

    # Get statistic name and row names
    stat <- ifelse("count_fraction" %in% names(s_x), "count_fraction", "unique")
    if ("flag_variables" %in% names(s_args)) {
      var_nms <- s_args$flag_variables
    } else if (!is.null(names(s_x[[stat]]))) {
      var_nms <- names(s_x[[stat]])
    } else {
      var_nms <- ""
      s_x[[stat]] <- list(s_x[[stat]])
      s_y[[stat]] <- list(s_y[[stat]])
    }

    # Calculate risk difference for each row, repeated if multiple statistics in table
    pct <- tail(strsplit(cur_split, "_")[[1]], 1) == "pct"
    rd_ci <- rep(stat_propdiff_ci(
      lapply(s_x[[stat]], `[`, 1), lapply(s_y[[stat]], `[`, 1),
      N_col_x, N_col_y,
      list_names = var_nms,
      pct = pct
    ), max(1, length(.stats)))

    in_rows(.list = rd_ci, .formats = "xx.x (xx.x - xx.x)", .indent_mods = .indent_mods)
  }
}

#' Tabulate Binary Response by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Tabulate statistics such as response rate and odds ratio for population subgroups.
#'
#' @inheritParams extract_rsp_subgroups
#' @inheritParams argument_convention
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. Tables typically used as part of forest plot.
#'
#' @seealso [extract_rsp_subgroups()]
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
#'   droplevels() %>%
#'   mutate(
#'     # Reorder levels of factor to make the placebo group the reference arm.
#'     ARM = fct_relevel(ARM, "B: Placebo"),
#'     rsp = AVALC == "CR"
#'   )
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' # Unstratified analysis.
#' df <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f
#' )
#' df
#'
#' # Stratified analysis.
#' df_strat <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2"), strata = "STRATA1"),
#'   data = adrs_f
#' )
#' df_strat
#'
#' # Grouping of the BMRKR2 levels.
#' df_grouped <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#' df_grouped
#'
#' @name response_subgroups
#' @order 1
NULL

#' Prepares Response Data for Population Subgroups in Data Frames
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares response rates and odds ratios for population subgroups in data frames. Simple wrapper
#' for [h_odds_ratio_subgroups_df()] and [h_proportion_subgroups_df()]. Result is a list of two
#' `data.frames`: `prop` and `or`. `variables` corresponds to the names of variables found in `data`,
#' passed as a named `list` and requires elements `rsp`, `arm` and optionally `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams response_subgroups
#' @param label_all (`string`)\cr label for the total population analysis.
#'
#' @return A named list of two elements:
#'   * `prop`: A `data.frame` containing columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`, `var`,
#'     `var_label`, and `row_type`.
#'   * `or`: A `data.frame` containing columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`,
#'     `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @seealso [response_subgroups]
#'
#' @export
extract_rsp_subgroups <- function(variables,
                                  data,
                                  groups_lists = list(),
                                  conf_level = 0.95,
                                  method = NULL,
                                  label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_rsp_subgroups() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  df_prop <- h_proportion_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    label_all = label_all
  )
  df_or <- h_odds_ratio_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    conf_level = conf_level,
    method = method,
    label_all = label_all
  )

  list(prop = df_prop, or = df_or)
}

#' @describeIn response_subgroups Formatted analysis function which is used as `afun` in `tabulate_rsp_subgroups()`.
#'
#' @return
#' * `a_response_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_response_subgroups <- function(.formats = list(
                                   n = "xx", # nolint start
                                   n_rsp = "xx",
                                   prop = "xx.x%",
                                   n_tot = "xx",
                                   or = list(format_extreme_values(2L)),
                                   ci = list(format_extreme_values_ci(2L)),
                                   pval = "x.xxxx | (<0.0001)" # nolint end
                                 ),
                                 na_str = default_na_str()) {
  checkmate::assert_list(.formats)
  checkmate::assert_subset(
    names(.formats),
    c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval")
  )

  afun_lst <- Map(
    function(stat, fmt, na_str) {
      if (stat == "ci") {
        function(df, labelstr = "", ...) {
          in_rows(
            .list = combine_vectors(df$lcl, df$ucl),
            .labels = as.character(df$subgroup),
            .formats = fmt,
            .format_na_strs = na_str
          )
        }
      } else {
        function(df, labelstr = "", ...) {
          in_rows(
            .list = as.list(df[[stat]]),
            .labels = as.character(df$subgroup),
            .formats = fmt,
            .format_na_strs = na_str
          )
        }
      }
    },
    stat = names(.formats),
    fmt = .formats,
    na_str = na_str
  )

  afun_lst
}

#' @describeIn response_subgroups Table-creating function which creates a table
#'   summarizing binary response by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
#'   and [rtables::summarize_row_groups()].
#'
#' @param df (`list`)\cr of data frames containing all analysis variables. List should be
#'   created using [extract_rsp_subgroups()].
#' @param vars (`character`)\cr the names of statistics to be reported among:
#'   * `n`: Total number of observations per group.
#'   * `n_rsp`: Number of responders per group.
#'   * `prop`: Proportion of responders.
#'   * `n_tot`: Total number of observations.
#'   * `or`: Odds ratio.
#'   * `ci` : Confidence interval of odds ratio.
#'   * `pval`: p-value of the effect.
#'   Note, the statistics `n_tot`, `or` and `ci` are required.
#'
#' @return An `rtables` table summarizing binary response by subgroup.
#'
#' @examples
#' ## Table with default columns.
#' basic_table() %>%
#'   tabulate_rsp_subgroups(df)
#'
#' ## Table with selected columns.
#' basic_table() %>%
#'   tabulate_rsp_subgroups(
#'     df = df,
#'     vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci")
#'   )
#'
#' @export
#' @order 2
tabulate_rsp_subgroups <- function(lyt,
                                   df,
                                   vars = c("n_tot", "n", "prop", "or", "ci"),
                                   groups_lists = list(),
                                   label_all = "All Patients",
                                   na_str = default_na_str()) {
  conf_level <- df$or$conf_level[1]
  method <- if ("pval_label" %in% names(df$or)) {
    df$or$pval_label[1]
  } else {
    NULL
  }

  extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method, label_all = label_all)

  afun_lst <- a_response_subgroups(na_str = na_str)
  colvars <- d_rsp_subgroups_colvars(vars, conf_level = conf_level, method = method)

  colvars_prop <- list(
    vars = colvars$vars[names(colvars$labels) %in% c("n", "prop", "n_rsp")],
    labels = colvars$labels[names(colvars$labels) %in% c("n", "prop", "n_rsp")]
  )
  colvars_or <- list(
    vars = colvars$vars[names(colvars$labels) %in% c("n_tot", "or", "ci", "pval")],
    labels = colvars$labels[names(colvars$labels) %in% c("n_tot", "or", "ci", "pval")]
  )

  # Columns from table_prop are optional.
  if (length(colvars_prop$vars) > 0) {
    lyt_prop <- split_cols_by(lyt = lyt, var = "arm")
    lyt_prop <- split_cols_by_multivar(
      lyt = lyt_prop,
      vars = colvars_prop$vars,
      varlabels = colvars_prop$labels
    )

    # "All Patients" row
    lyt_prop <- split_rows_by(
      lyt = lyt_prop,
      var = "row_type",
      split_fun = keep_split_levels("content"),
      nested = FALSE,
      child_labels = "hidden"
    )
    lyt_prop <- analyze_colvars(
      lyt = lyt_prop,
      afun = afun_lst[names(colvars_prop$labels)],
      na_str = na_str,
      extra_args = extra_args
    )

    if ("analysis" %in% df$prop$row_type) {
      lyt_prop <- split_rows_by(
        lyt = lyt_prop,
        var = "row_type",
        split_fun = keep_split_levels("analysis"),
        nested = FALSE,
        child_labels = "hidden"
      )
      lyt_prop <- split_rows_by(lyt = lyt_prop, var = "var_label", nested = TRUE)
      lyt_prop <- analyze_colvars(
        lyt = lyt_prop,
        afun = afun_lst[names(colvars_prop$labels)],
        na_str = na_str,
        inclNAs = TRUE,
        extra_args = extra_args
      )
    }

    table_prop <- build_table(lyt_prop, df = df$prop)
  } else {
    table_prop <- NULL
  }

  # Columns "n_tot", "or", "ci" in table_or are required.
  lyt_or <- split_cols_by(lyt = lyt, var = "arm")
  lyt_or <- split_cols_by_multivar(
    lyt = lyt_or,
    vars = colvars_or$vars,
    varlabels = colvars_or$labels
  )

  # "All Patients" row
  lyt_or <- split_rows_by(
    lyt = lyt_or,
    var = "row_type",
    split_fun = keep_split_levels("content"),
    nested = FALSE,
    child_labels = "hidden"
  )
  lyt_or <- analyze_colvars(
    lyt = lyt_or,
    afun = afun_lst[names(colvars_or$labels)],
    na_str = na_str,
    extra_args = extra_args
  ) %>%
    append_topleft("Baseline Risk Factors")

  if ("analysis" %in% df$or$row_type) {
    lyt_or <- split_rows_by(
      lyt = lyt_or,
      var = "row_type",
      split_fun = keep_split_levels("analysis"),
      nested = FALSE,
      child_labels = "hidden"
    )
    lyt_or <- split_rows_by(lyt = lyt_or, var = "var_label", nested = TRUE)
    lyt_or <- analyze_colvars(
      lyt = lyt_or,
      afun = afun_lst[names(colvars_or$labels)],
      na_str = na_str,
      inclNAs = TRUE,
      extra_args = extra_args
    )
  }
  table_or <- build_table(lyt_or, df = df$or)

  n_tot_id <- match("n_tot", colvars_or$vars)
  if (is.null(table_prop)) {
    result <- table_or
    or_id <- match("or", colvars_or$vars)
    ci_id <- match("lcl", colvars_or$vars)
  } else {
    result <- cbind_rtables(table_or[, n_tot_id], table_prop, table_or[, -n_tot_id])
    or_id <- 1L + ncol(table_prop) + match("or", colvars_or$vars[-n_tot_id])
    ci_id <- 1L + ncol(table_prop) + match("lcl", colvars_or$vars[-n_tot_id])
    n_tot_id <- 1L
  }
  structure(
    result,
    forest_header = paste0(levels(df$prop$arm), "\nBetter"),
    col_x = or_id,
    col_ci = ci_id,
    col_symbol_size = n_tot_id
  )
}

#' Labels for Column Variables in Binary Response by Subgroup Table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Internal function to check variables included in [tabulate_rsp_subgroups()] and create column labels.
#'
#' @inheritParams argument_convention
#' @inheritParams tabulate_rsp_subgroups
#'
#' @return A `list` of variables to tabulate and their labels.
#'
#' @export
d_rsp_subgroups_colvars <- function(vars,
                                    conf_level = NULL,
                                    method = NULL) {
  checkmate::assert_character(vars)
  checkmate::assert_subset(c("n_tot", "or", "ci"), vars)
  checkmate::assert_subset(
    vars,
    c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval")
  )

  varlabels <- c(
    n = "n",
    n_rsp = "Responders",
    prop = "Response (%)",
    n_tot = "Total n",
    or = "Odds Ratio"
  )
  colvars <- vars

  if ("ci" %in% colvars) {
    checkmate::assert_false(is.null(conf_level))

    varlabels <- c(
      varlabels,
      ci = paste0(100 * conf_level, "% CI")
    )

    # The `lcl`` variable is just a placeholder available in the analysis data,
    # it is not acutally used in the tabulation.
    # Variables used in the tabulation are lcl and ucl, see `a_response_subgroups` for details.
    colvars[colvars == "ci"] <- "lcl"
  }

  if ("pval" %in% colvars) {
    varlabels <- c(
      varlabels,
      pval = method
    )
  }

  list(
    vars = colvars,
    labels = varlabels[vars]
  )
}

#' Create a STEP Graph
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Based on the STEP results, creates a `ggplot` graph showing the estimated HR or OR
#' along the continuous biomarker value subgroups.
#'
#' @param df (`tibble`)\cr result of [tidy.step()].
#' @param use_percentile (`flag`)\cr whether to use percentiles for the x axis or actual
#'   biomarker values.
#' @param est (named `list`)\cr `col` and `lty` settings for estimate line.
#' @param ci_ribbon (named `list` or `NULL`)\cr `fill` and `alpha` settings for the confidence interval
#'   ribbon area, or `NULL` to not plot a CI ribbon.
#' @param col (`character`)\cr colors.
#'
#' @return A `ggplot` STEP graph.
#'
#' @seealso Custom tidy method [tidy.step()].
#'
#' @examples
#' library(nestcolor)
#' library(survival)
#' lung$sex <- factor(lung$sex)
#'
#' # Survival example.
#' vars <- list(
#'   time = "time",
#'   event = "status",
#'   arm = "sex",
#'   biomarker = "age"
#' )
#'
#' step_matrix <- fit_survival_step(
#'   variables = vars,
#'   data = lung,
#'   control = c(control_coxph(), control_step(num_points = 10, degree = 2))
#' )
#' step_data <- broom::tidy(step_matrix)
#'
#' # Default plot.
#' g_step(step_data)
#'
#' # Add the reference 1 horizontal line.
#' library(ggplot2)
#' g_step(step_data) +
#'   ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2)
#'
#' # Use actual values instead of percentiles, different color for estimate and no CI,
#' # use log scale for y axis.
#' g_step(
#'   step_data,
#'   use_percentile = FALSE,
#'   est = list(col = "blue", lty = 1),
#'   ci_ribbon = NULL
#' ) + scale_y_log10()
#'
#' # Adding another curve based on additional column.
#' step_data$extra <- exp(step_data$`Percentile Center`)
#' g_step(step_data) +
#'   ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = "green")
#'
#' # Response example.
#' vars <- list(
#'   response = "status",
#'   arm = "sex",
#'   biomarker = "age"
#' )
#'
#' step_matrix <- fit_rsp_step(
#'   variables = vars,
#'   data = lung,
#'   control = c(
#'     control_logistic(response_definition = "I(response == 2)"),
#'     control_step()
#'   )
#' )
#' step_data <- broom::tidy(step_matrix)
#' g_step(step_data)
#'
#' @export
g_step <- function(df,
                   use_percentile = "Percentile Center" %in% names(df),
                   est = list(col = "blue", lty = 1),
                   ci_ribbon = list(fill = getOption("ggplot2.discrete.colour")[1], alpha = 0.5),
                   col = getOption("ggplot2.discrete.colour")) {
  checkmate::assert_tibble(df)
  checkmate::assert_flag(use_percentile)
  checkmate::assert_character(col, null.ok = TRUE)
  checkmate::assert_list(est, names = "named")
  checkmate::assert_list(ci_ribbon, names = "named", null.ok = TRUE)

  x_var <- ifelse(use_percentile, "Percentile Center", "Interval Center")
  df$x <- df[[x_var]]
  attrs <- attributes(df)
  df$y <- df[[attrs$estimate]]

  # Set legend names. To be modified also at call level
  legend_names <- c("Estimate", "CI 95%")

  p <- ggplot2::ggplot(df, ggplot2::aes(x = .data[["x"]], y = .data[["y"]]))

  if (!is.null(col)) {
    p <- p +
      ggplot2::scale_color_manual(values = col)
  }

  if (!is.null(ci_ribbon)) {
    if (is.null(ci_ribbon$fill)) {
      ci_ribbon$fill <- "lightblue"
    }
    p <- p + ggplot2::geom_ribbon(
      ggplot2::aes(
        ymin = .data[["ci_lower"]], ymax = .data[["ci_upper"]],
        fill = legend_names[2]
      ),
      alpha = ci_ribbon$alpha
    ) +
      scale_fill_manual(
        name = "", values = c("CI 95%" = ci_ribbon$fill)
      )
  }
  suppressMessages(p <- p +
    ggplot2::geom_line(
      ggplot2::aes(y = .data[["y"]], color = legend_names[1]),
      linetype = est$lty
    ) +
    scale_colour_manual(
      name = "", values = c("Estimate" = "blue")
    ))

  p <- p + ggplot2::labs(x = attrs$biomarker, y = attrs$estimate)
  if (use_percentile) {
    p <- p + ggplot2::scale_x_continuous(labels = scales::percent)
  }
  p
}

#' Custom Tidy Method for STEP Results
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Tidy the STEP results into a `tibble` format ready for plotting.
#'
#' @param x (`step` matrix)\cr results from [fit_survival_step()].
#' @param ... not used here.
#'
#' @return A `tibble` with one row per STEP subgroup. The estimates and CIs are on the HR or OR scale,
#'   respectively. Additional attributes carry metadata also used for plotting.
#'
#' @seealso [g_step()] which consumes the result from this function.
#'
#' @method tidy step
#'
#' @examples
#' library(survival)
#' lung$sex <- factor(lung$sex)
#' vars <- list(
#'   time = "time",
#'   event = "status",
#'   arm = "sex",
#'   biomarker = "age"
#' )
#' step_matrix <- fit_survival_step(
#'   variables = vars,
#'   data = lung,
#'   control = c(control_coxph(), control_step(num_points = 10, degree = 2))
#' )
#' broom::tidy(step_matrix)
#'
#' @export
tidy.step <- function(x, ...) { # nolint
  checkmate::assert_class(x, "step")
  dat <- as.data.frame(x)
  nams <- names(dat)
  is_surv <- "loghr" %in% names(dat)
  est_var <- ifelse(is_surv, "loghr", "logor")
  new_est_var <- ifelse(is_surv, "Hazard Ratio", "Odds Ratio")
  new_y_vars <- c(new_est_var, c("ci_lower", "ci_upper"))
  names(dat)[match(est_var, nams)] <- new_est_var
  dat[, new_y_vars] <- exp(dat[, new_y_vars])
  any_is_na <- any(is.na(dat[, new_y_vars]))
  any_is_very_large <- any(abs(dat[, new_y_vars]) > 1e10, na.rm = TRUE)
  if (any_is_na) {
    warning(paste(
      "Missing values in the point estimate or CI columns,",
      "this will lead to holes in the `g_step()` plot"
    ))
  }
  if (any_is_very_large) {
    warning(paste(
      "Very large absolute values in the point estimate or CI columns,",
      "consider adding `scale_y_log10()` to the `g_step()` result for plotting"
    ))
  }
  if (any_is_na || any_is_very_large) {
    warning("Consider using larger `bandwidth`, less `num_points` in `control_step()` settings for fitting")
  }
  structure(
    tibble::as_tibble(dat),
    estimate = new_est_var,
    biomarker = attr(x, "variables")$biomarker,
    ci = f_conf_level(attr(x, "control")$conf_level)
  )
}

#' Get default statistical methods and their associated formats, labels, and indent modifiers
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Utility functions to get valid statistic methods for different method groups
#' (`.stats`) and their associated formats (`.formats`), labels (`.labels`), and indent modifiers
#' (`.indent_mods`). This utility is used across `tern`, but some of its working principles can be
#' seen in [analyze_vars()]. See notes to understand why this is experimental.
#'
#' @param stats (`character`)\cr statistical methods to get defaults for.
#'
#' @details
#' Current choices for `type` are `counts` and `numeric` for [analyze_vars()] and affect `get_stats()`.
#'
#' @note
#' These defaults are experimental because we use the names of functions to retrieve the default
#' statistics. This should be generalized in groups of methods according to more reasonable groupings.
#'
#' @name default_stats_formats_labels
NULL

#' @describeIn default_stats_formats_labels Get statistics available for a given method
#'   group (analyze function).
#'
#' @param method_groups (`character`)\cr indicates the statistical method group (`tern` analyze function)
#'   to retrieve default statistics for. A character vector can be used to specify more than one statistical
#'   method group.
#' @param stats_in (`character`)\cr statistics to retrieve for the selected method group.
#' @param add_pval (`flag`)\cr should `"pval"` (or `"pval_counts"` if `method_groups` contains
#'   `"analyze_vars_counts"`) be added to the statistical methods?
#'
#' @return
#' * `get_stats()` returns a `character` vector of statistical methods.
#'
#' @examples
#' # analyze_vars is numeric
#' num_stats <- get_stats("analyze_vars_numeric") # also the default
#'
#' # Other type
#' cnt_stats <- get_stats("analyze_vars_counts")
#'
#' # Weirdly taking the pval from count_occurrences
#' only_pval <- get_stats("count_occurrences", add_pval = TRUE, stats_in = "pval")
#'
#' # All count_occurrences
#' all_cnt_occ <- get_stats("count_occurrences")
#'
#' # Multiple
#' get_stats(c("count_occurrences", "analyze_vars_counts"))
#'
#' @export
get_stats <- function(method_groups = "analyze_vars_numeric", stats_in = NULL, add_pval = FALSE) {
  checkmate::assert_character(method_groups)
  checkmate::assert_character(stats_in, null.ok = TRUE)
  checkmate::assert_flag(add_pval)

  # Default is still numeric
  if (any(method_groups == "analyze_vars")) {
    method_groups[method_groups == "analyze_vars"] <- "analyze_vars_numeric"
  }

  type_tmp <- ifelse(any(grepl("counts", method_groups)), "counts", "numeric") # for pval checks

  # Defaults for loop
  out <- NULL

  # Loop for multiple method groups
  for (mgi in method_groups) {
    out_tmp <- if (mgi %in% names(tern_default_stats)) {
      tern_default_stats[[mgi]]
    } else {
      stop("The selected method group (", mgi, ") has no default statistical method.")
    }
    out <- unique(c(out, out_tmp))
  }

  # If you added pval to the stats_in you certainly want it
  if (!is.null(stats_in) && any(grepl("^pval", stats_in))) {
    stats_in_pval_value <- stats_in[grepl("^pval", stats_in)]

    # Must be only one value between choices
    checkmate::assert_choice(stats_in_pval_value, c("pval", "pval_counts"))

    # Mismatch with counts and numeric
    if (any(grepl("counts", method_groups)) && stats_in_pval_value != "pval_counts" ||
      any(grepl("numeric", method_groups)) && stats_in_pval_value != "pval") { # nolint
      stop(
        "Inserted p-value (", stats_in_pval_value, ") is not valid for type ",
        type_tmp, ". Use ", paste(ifelse(stats_in_pval_value == "pval", "pval_counts", "pval")),
        " instead."
      )
    }

    # Lets add it even if present (thanks to unique)
    add_pval <- TRUE
  }

  # Mainly used in "analyze_vars" but it could be necessary elsewhere
  if (isTRUE(add_pval)) {
    if (any(grepl("counts", method_groups))) {
      out <- unique(c(out, "pval_counts"))
    } else {
      out <- unique(c(out, "pval"))
    }
  }

  # Filtering for stats_in (character vector)
  if (!is.null(stats_in)) {
    out <- intersect(stats_in, out) # It orders them too
  }

  # If intersect did not find matches (and no pval?) -> error
  if (length(out) == 0) {
    stop(
      "The selected method group(s) (", paste0(method_groups, collapse = ", "), ")",
      " do not have the required default statistical methods:\n",
      paste0(stats_in, collapse = " ")
    )
  }

  out
}

#' @describeIn default_stats_formats_labels Get formats corresponding to a list of statistics.
#'
#' @param formats_in (named `vector`) \cr inserted formats to replace defaults. It can be a
#'   character vector from [formatters::list_valid_format_labels()] or a custom format function.
#'
#' @return
#' * `get_formats_from_stats()` returns a named vector of formats (if present in either
#'   `tern_default_formats` or `formats_in`, otherwise `NULL`). Values can be taken from
#'   [formatters::list_valid_format_labels()] or a custom function (e.g. [formatting_functions]).
#'
#' @note Formats in `tern` and `rtables` can be functions that take in the table cell value and
#'   return a string. This is well documented in `vignette("custom_appearance", package = "rtables")`.
#'
#' @examples
#' # Defaults formats
#' get_formats_from_stats(num_stats)
#' get_formats_from_stats(cnt_stats)
#' get_formats_from_stats(only_pval)
#' get_formats_from_stats(all_cnt_occ)
#'
#' # Addition of customs
#' get_formats_from_stats(all_cnt_occ, formats_in = c("fraction" = c("xx")))
#' get_formats_from_stats(all_cnt_occ, formats_in = list("fraction" = c("xx.xx", "xx")))
#'
#' @seealso [formatting_functions]
#'
#' @export
get_formats_from_stats <- function(stats, formats_in = NULL) {
  checkmate::assert_character(stats, min.len = 1)
  # It may be a list if there is a function in the formats
  if (checkmate::test_list(formats_in, null.ok = TRUE)) {
    checkmate::assert_list(formats_in, null.ok = TRUE)
    # Or it may be a vector of characters
  } else {
    checkmate::assert_character(formats_in, null.ok = TRUE)
  }

  # Extract global defaults
  which_fmt <- match(stats, names(tern_default_formats))

  # Select only needed formats from stats
  ret <- vector("list", length = length(stats)) # Returning a list is simpler
  ret[!is.na(which_fmt)] <- tern_default_formats[which_fmt[!is.na(which_fmt)]]

  out <- setNames(ret, stats)

  # Modify some with custom formats
  if (!is.null(formats_in)) {
    # Stats is the main
    common_names <- intersect(names(out), names(formats_in))
    out[common_names] <- formats_in[common_names]
  }

  out
}

#' @describeIn default_stats_formats_labels Get labels corresponding to a list of statistics.
#'
#' @param labels_in (named `vector` of `character`)\cr inserted labels to replace defaults.
#' @param row_nms (`character`)\cr row names. Levels of a `factor` or `character` variable, each
#'   of which the statistics in `.stats` will be calculated for. If this parameter is set, these
#'   variable levels will be used as the defaults, and the names of the given custom values should
#'   correspond to levels (or have format `statistic.level`) instead of statistics. Can also be
#'   variable names if rows correspond to different variables instead of levels. Defaults to `NULL`.
#'
#' @return
#' * `get_labels_from_stats()` returns a named `character` vector of labels (if present in either
#'   `tern_default_labels` or `labels_in`, otherwise `NULL`).
#'
#' @examples
#' # Defaults labels
#' get_labels_from_stats(num_stats)
#' get_labels_from_stats(cnt_stats)
#' get_labels_from_stats(only_pval)
#' get_labels_from_stats(all_cnt_occ)
#'
#' # Addition of customs
#' get_labels_from_stats(all_cnt_occ, labels_in = c("fraction" = "Fraction"))
#' get_labels_from_stats(all_cnt_occ, labels_in = list("fraction" = c("Some more fractions")))
#'
#' @export
get_labels_from_stats <- function(stats, labels_in = NULL, row_nms = NULL) {
  checkmate::assert_character(stats, min.len = 1)
  checkmate::assert_character(row_nms, null.ok = TRUE)
  # It may be a list
  if (checkmate::test_list(labels_in, null.ok = TRUE)) {
    checkmate::assert_list(labels_in, null.ok = TRUE)
    # Or it may be a vector of characters
  } else {
    checkmate::assert_character(labels_in, null.ok = TRUE)
  }

  if (!is.null(row_nms)) {
    ret <- rep(row_nms, length(stats))
    out <- setNames(ret, paste(rep(stats, each = length(row_nms)), ret, sep = "."))

    if (!is.null(labels_in)) {
      lvl_lbls <- intersect(names(labels_in), row_nms)
      for (i in lvl_lbls) out[paste(stats, i, sep = ".")] <- labels_in[[i]]
    }
  } else {
    which_lbl <- match(stats, names(tern_default_labels))

    ret <- vector("character", length = length(stats)) # it needs to be a character vector
    ret[!is.na(which_lbl)] <- tern_default_labels[which_lbl[!is.na(which_lbl)]]

    out <- setNames(ret, stats)
  }

  # Modify some with custom labels
  if (!is.null(labels_in)) {
    # Stats is the main
    common_names <- intersect(names(out), names(labels_in))
    out[common_names] <- labels_in[common_names]
  }

  out
}

#' @describeIn default_stats_formats_labels Format indent modifiers for a given vector/list of statistics.
#'
#' @param indents_in (named `vector`)\cr inserted indent modifiers to replace defaults (default is `0L`).
#'
#' @return
#' * `get_indents_from_stats()` returns a single indent modifier value to apply to all rows
#'   or a named numeric vector of indent modifiers (if present, otherwise `NULL`).
#'
#' @examples
#' get_indents_from_stats(all_cnt_occ, indents_in = 3L)
#' get_indents_from_stats(all_cnt_occ, indents_in = list(count = 2L, count_fraction = 5L))
#' get_indents_from_stats(
#'   all_cnt_occ,
#'   indents_in = list(a = 2L, count.a = 1L, count.b = 5L), row_nms = c("a", "b")
#' )
#'
#' @export
get_indents_from_stats <- function(stats, indents_in = NULL, row_nms = NULL) {
  checkmate::assert_character(stats, min.len = 1)
  checkmate::assert_character(row_nms, null.ok = TRUE)
  # It may be a list
  if (checkmate::test_list(indents_in, null.ok = TRUE)) {
    checkmate::assert_list(indents_in, null.ok = TRUE)
    # Or it may be a vector of integers
  } else {
    checkmate::assert_integerish(indents_in, null.ok = TRUE)
  }

  if (is.null(names(indents_in)) && length(indents_in) == 1) {
    out <- rep(indents_in, length(stats) * if (!is.null(row_nms)) length(row_nms) else 1)
    return(out)
  }

  if (!is.null(row_nms)) {
    ret <- rep(0L, length(stats) * length(row_nms))
    out <- setNames(ret, paste(rep(stats, each = length(row_nms)), rep(row_nms, length(stats)), sep = "."))

    if (!is.null(indents_in)) {
      lvl_lbls <- intersect(names(indents_in), row_nms)
      for (i in lvl_lbls) out[paste(stats, i, sep = ".")] <- indents_in[[i]]
    }
  } else {
    ret <- rep(0L, length(stats))
    out <- setNames(ret, stats)
  }

  # Modify some with custom labels
  if (!is.null(indents_in)) {
    # Stats is the main
    common_names <- intersect(names(out), names(indents_in))
    out[common_names] <- indents_in[common_names]
  }

  out
}

#' Update Labels According to Control Specifications
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Given a list of statistic labels and and a list of control parameters, updates labels with a relevant
#' control specification. For example, if control has element `conf_level` set to `0.9`, the default
#' label for statistic `mean_ci` will be updated to `"Mean 90% CI"`. Any labels that are supplied
#' via `labels_custom` will not be updated regardless of `control`.
#'
#' @param labels_default (named `vector` of `character`)\cr a named vector of statistic labels to modify
#'   according to the control specifications. Labels that are explicitly defined in `labels_custom` will
#'   not be affected.
#' @param labels_custom (named `vector` of `character`)\cr named vector of labels that are customized by
#'   the user and should not be affected by `control`.
#' @param control (named `list`)\cr list of control parameters to apply to adjust default labels.
#'
#' @return A named character vector of labels with control specifications applied to relevant labels.
#'
#' @examples
#' control <- list(conf_level = 0.80, quantiles = c(0.1, 0.83), test_mean = 0.57)
#' get_labels_from_stats(c("mean_ci", "quantiles", "mean_pval")) %>%
#'   labels_use_control(control = control)
#'
#' @export
labels_use_control <- function(labels_default, control, labels_custom = NULL) {
  if ("conf_level" %in% names(control)) {
    labels_default <- sapply(
      names(labels_default),
      function(x) {
        if (!x %in% names(labels_custom)) {
          gsub(labels_default[[x]], pattern = "[0-9]+% CI", replacement = f_conf_level(control[["conf_level"]]))
        } else {
          labels_default[[x]]
        }
      }
    )
  }
  if ("quantiles" %in% names(control) && "quantiles" %in% names(labels_default) &&
    !"quantiles" %in% names(labels_custom)) { # nolint
    labels_default["quantiles"] <- gsub(
      "[0-9]+% and [0-9]+", paste0(control[["quantiles"]][1] * 100, "% and ", control[["quantiles"]][2] * 100, ""),
      labels_default["quantiles"]
    )
  }
  if ("test_mean" %in% names(control) && "mean_pval" %in% names(labels_default) &&
    !"mean_pval" %in% names(labels_custom)) { # nolint
    labels_default["mean_pval"] <- gsub(
      "p-value \\(H0: mean = [0-9\\.]+\\)", f_pval(control[["test_mean"]]), labels_default["mean_pval"]
    )
  }

  labels_default
}

#' @describeIn default_stats_formats_labels Named list of available statistics by method group for `tern`.
#'
#' @format
#' * `tern_default_stats` is a named list of available statistics, with each element
#'   named for their corresponding statistical method group.
#'
#' @export
tern_default_stats <- list(
  abnormal = c("fraction"),
  abnormal_by_baseline = c("fraction"),
  abnormal_by_marked = c("count_fraction", "count_fraction_fixed_dp"),
  abnormal_by_worst_grade = c("count_fraction", "count_fraction_fixed_dp"),
  abnormal_by_worst_grade_worsen = c("fraction"),
  analyze_patients_exposure_in_cols = c("n_patients", "sum_exposure"),
  analyze_vars_counts = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "n_blq"),
  analyze_vars_numeric = c(
    "n", "sum", "mean", "sd", "se", "mean_sd", "mean_se", "mean_ci", "mean_sei", "mean_sdi", "mean_pval",
    "median", "mad", "median_ci", "quantiles", "iqr", "range", "min", "max", "median_range", "cv",
    "geom_mean", "geom_mean_ci", "geom_cv"
  ),
  count_cumulative = c("count_fraction", "count_fraction_fixed_dp"),
  count_missed_doses = c("n", "count_fraction", "count_fraction_fixed_dp"),
  count_occurrences = c("count", "count_fraction", "count_fraction_fixed_dp", "fraction"),
  count_occurrences_by_grade = c("count_fraction", "count_fraction_fixed_dp"),
  count_patients_with_event = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "n_blq"),
  count_patients_with_flags = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "n_blq"),
  count_values = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "n_blq"),
  coxph_pairwise = c("pvalue", "hr", "hr_ci", "n_tot", "n_tot_events"),
  estimate_incidence_rate = c("person_years", "n_events", "rate", "rate_ci"),
  estimate_multinomial_response = c("n_prop", "prop_ci"),
  estimate_odds_ratio = c("or_ci", "n_tot"),
  estimate_proportion = c("n_prop", "prop_ci"),
  estimate_proportion_diff = c("diff", "diff_ci"),
  summarize_ancova = c("n", "lsmean", "lsmean_diff", "lsmean_diff_ci", "pval"),
  summarize_coxreg = c("n", "hr", "ci", "pval", "pval_inter"),
  summarize_glm_count = c("n", "rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
  summarize_num_patients = c("unique", "nonunique", "unique_count"),
  summarize_patients_events_in_cols = c("unique", "all"),
  surv_time = c("median", "median_ci", "quantiles", "range_censor", "range_event", "range"),
  surv_timepoint = c("pt_at_risk", "event_free_rate", "rate_se", "rate_ci", "rate_diff", "rate_diff_ci", "ztest_pval"),
  tabulate_rsp_biomarkers = c("n_tot", "n_rsp", "prop", "or", "ci", "pval"),
  tabulate_rsp_subgroups = c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval"),
  tabulate_survival_biomarkers = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
  tabulate_survival_subgroups = c("n_tot_events", "n_events", "n_tot", "n", "median", "hr", "ci", "pval"),
  test_proportion_diff = c("pval")
)

#' @describeIn default_stats_formats_labels Named vector of default formats for `tern`.
#'
#' @format
#' * `tern_default_formats` is a named vector of available default formats, with each element
#'   named for their corresponding statistic.
#'
#' @export
tern_default_formats <- c(
  fraction = format_fraction_fixed_dp,
  unique = format_count_fraction_fixed_dp,
  nonunique = "xx",
  unique_count = "xx",
  n = "xx.",
  count = "xx.",
  count_fraction = format_count_fraction,
  count_fraction_fixed_dp = format_count_fraction_fixed_dp,
  n_blq = "xx.",
  sum = "xx.x",
  mean = "xx.x",
  sd = "xx.x",
  se = "xx.x",
  mean_sd = "xx.x (xx.x)",
  mean_se = "xx.x (xx.x)",
  mean_ci = "(xx.xx, xx.xx)",
  mean_sei = "(xx.xx, xx.xx)",
  mean_sdi = "(xx.xx, xx.xx)",
  mean_pval = "x.xxxx | (<0.0001)",
  median = "xx.x",
  mad = "xx.x",
  median_ci = "(xx.xx, xx.xx)",
  quantiles = "xx.x - xx.x",
  iqr = "xx.x",
  range = "xx.x - xx.x",
  min = "xx.x",
  max = "xx.x",
  median_range = "xx.x (xx.x - xx.x)",
  cv = "xx.x",
  geom_mean = "xx.x",
  geom_mean_ci = "(xx.xx, xx.xx)",
  geom_cv = "xx.x",
  pval = "x.xxxx | (<0.0001)",
  pval_counts = "x.xxxx | (<0.0001)",
  range_censor = "xx.x to xx.x",
  range_event = "xx.x to xx.x"
)

#' @describeIn default_stats_formats_labels Named `character` vector of default labels for `tern`.
#'
#' @format
#' * `tern_default_labels` is a named `character` vector of available default labels, with each element
#'   named for their corresponding statistic.
#'
#' @export
tern_default_labels <- c(
  fraction = "fraction",
  unique = "Number of patients with at least one event",
  nonunique = "Number of events",
  n = "n",
  count = "count",
  count_fraction = "count_fraction",
  count_fraction_fixed_dp = "count_fraction",
  n_blq = "n_blq",
  sum = "Sum",
  mean = "Mean",
  sd = "SD",
  se = "SE",
  mean_sd = "Mean (SD)",
  mean_se = "Mean (SE)",
  mean_ci = "Mean 95% CI",
  mean_sei = "Mean -/+ 1xSE",
  mean_sdi = "Mean -/+ 1xSD",
  mean_pval = "Mean p-value (H0: mean = 0)",
  median = "Median",
  mad = "Median Absolute Deviation",
  median_ci = "Median 95% CI",
  quantiles = "25% and 75%-ile",
  iqr = "IQR",
  range = "Min - Max",
  min = "Minimum",
  max = "Maximum",
  median_range = "Median (Min - Max)",
  cv = "CV (%)",
  geom_mean = "Geometric Mean",
  geom_mean_ci = "Geometric Mean 95% CI",
  geom_cv = "CV % Geometric Mean",
  pval = "p-value (t-test)", # Default for numeric
  pval_counts = "p-value (chi-squared test)" # Default for counts
)

# To deprecate ---------

#' @describeIn default_stats_formats_labels Quick function to retrieve default formats for summary statistics:
#'   [analyze_vars()] and [analyze_vars_in_cols()] principally.
#'
#' @param type (`flag`)\cr is it going to be `"numeric"` or `"counts"`?
#'
#' @return
#' * `summary_formats()` returns a named `vector` of default statistic formats for the given data type.
#'
#' @examples
#' summary_formats()
#' summary_formats(type = "counts", include_pval = TRUE)
#'
#' @export
summary_formats <- function(type = "numeric", include_pval = FALSE) {
  met_grp <- paste0(c("analyze_vars", type), collapse = "_")
  get_formats_from_stats(get_stats(met_grp, add_pval = include_pval))
}

#' @describeIn default_stats_formats_labels Quick function to retrieve default labels for summary statistics.
#'   Returns labels of descriptive statistics which are understood by `rtables`. Similar to `summary_formats`
#'
#' @param include_pval (`flag`)\cr deprecated parameter. Same as `add_pval`.
#' @return
#' * `summary_labels` returns a named `vector` of default statistic labels for the given data type.
#'
#' @examples
#' summary_labels()
#' summary_labels(type = "counts", include_pval = TRUE)
#'
#' @export
summary_labels <- function(type = "numeric", include_pval = FALSE) {
  met_grp <- paste0(c("analyze_vars", type), collapse = "_")
  get_labels_from_stats(get_stats(met_grp, add_pval = include_pval))
}

#' Line plot with the optional table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Line plot with the optional table.
#'
#' @param df (`data.frame`)\cr data set containing all analysis variables.
#' @param alt_counts_df (`data.frame` or `NULL`)\cr data set that will be used (only)
#'   to counts objects in groups for stratification.
#' @param variables (named `character` vector) of variable names in `df` data set. Details are:
#'   * `x` (`character`)\cr name of x-axis variable.
#'   * `y` (`character`)\cr name of y-axis variable.
#'   * `group_var` (`character`)\cr name of grouping variable (or strata), i.e. treatment arm.
#'     Can be `NA` to indicate lack of groups.
#'   * `subject_var` (`character`)\cr name of subject variable. Only applies if `group_var` is
#'      not NULL.
#'   * `paramcd` (`character`)\cr name of the variable for parameter's code. Used for y-axis label and plot's subtitle.
#'     Can be `NA` if `paramcd` is not to be added to the y-axis label or subtitle.
#'   * `y_unit` (`character`)\cr name of variable with units of `y`. Used for y-axis label and plot's subtitle.
#'     Can be `NA` if y unit is not to be added to the y-axis label or subtitle.
#' @param mid (`character` or `NULL`)\cr names of the statistics that will be plotted as midpoints.
#'   All the statistics indicated in `mid` variable must be present in the object returned by `sfun`,
#'   and be of a `double` or `numeric` type vector of length one.
#' @param interval (`character` or `NULL`)\cr names of the statistics that will be plotted as intervals.
#'   All the statistics indicated in `interval` variable must be present in the object returned by `sfun`,
#'   and be of a `double` or `numeric` type vector of length two. Set `interval = NULL` if intervals should not be
#'   added to the plot.
#' @param whiskers (`character`)\cr names of the interval whiskers that will be plotted. Names must match names
#'   of the list element `interval` that will be returned by `sfun` (e.g. `mean_ci_lwr` element of
#'   `sfun(x)[["mean_ci"]]`). It is possible to specify one whisker only, or to suppress all whiskers by setting
#'   `interval = NULL`.
#' @param table (`character` or `NULL`)\cr names of the statistics that will be displayed in the table below the plot.
#'   All the statistics indicated in `table` variable must be present in the object returned by `sfun`.
#' @param sfun (`closure`)\cr the function to compute the values of required statistics. It must return a named `list`
#'   with atomic vectors. The names of the `list` elements refer to the names of the statistics and are used by `mid`,
#'   `interval`, `table`. It must be able to accept as input a vector with data for which statistics are computed.
#' @param ... optional arguments to `sfun`.
#' @param mid_type (`character`)\cr controls the type of the `mid` plot, it can be point (`p`), line (`l`),
#'   or point and line (`pl`).
#' @param mid_point_size (`integer` or `double`)\cr controls the font size of the point for `mid` plot.
#' @param position (`character` or `call`)\cr geom element position adjustment, either as a string, or the result of
#'   a call to a position adjustment function.
#' @param legend_title (`character` string)\cr legend title.
#' @param legend_position (`character`)\cr the position of the plot legend (`none`, `left`, `right`, `bottom`, `top`,
#'   or two-element numeric vector).
#' @param ggtheme (`theme`)\cr a graphical theme as provided by `ggplot2` to control styling of the plot.
#' @param x_lab (`character`)\cr x-axis label. If equal to `NULL`, then no label will be added.
#' @param y_lab (`character`)\cr y-axis label. If equal to `NULL`, then no label will be added.
#' @param y_lab_add_paramcd (`logical`)\cr should `paramcd`, i.e. `unique(df[[variables["paramcd"]]])` be added to the
#'   y-axis label `y_lab`?
#' @param y_lab_add_unit (`logical`)\cr should y unit, i.e. `unique(df[[variables["y_unit"]]])` be added to the y-axis
#'   label `y_lab`?
#' @param title (`character`)\cr plot title.
#' @param subtitle (`character`)\cr plot subtitle.
#' @param subtitle_add_paramcd (`logical`)\cr should `paramcd`, i.e. `unique(df[[variables["paramcd"]]])` be added to
#'   the plot's subtitle `subtitle`?
#' @param subtitle_add_unit (`logical`)\cr should y unit, i.e. `unique(df[[variables["y_unit"]]])` be added to the
#'   plot's subtitle `subtitle`?
#' @param caption (`character`)\cr optional caption below the plot.
#' @param table_format (named `character` or `NULL`)\cr format patterns for descriptive statistics used in the
#'   (optional) table appended to the plot. It is passed directly to the `h_format_row` function through the `format`
#'   parameter. Names of `table_format` must match the names of statistics returned by `sfun` function.
#' @param table_labels (named `character` or `NULL`)\cr labels for descriptive statistics used in the (optional) table
#'   appended to the plot. Names of `table_labels` must match the names of statistics returned by `sfun` function.
#' @param table_font_size (`integer` or `double`)\cr controls the font size of values in the table.
#' @param newpage (`logical`)\cr should plot be drawn on new page?
#' @param col (`character`)\cr colors.
#'
#' @return A `ggplot` line plot (and statistics table if applicable).
#'
#' @examples
#' library(nestcolor)
#'
#' adsl <- tern_ex_adsl
#' adlb <- tern_ex_adlb %>% dplyr::filter(ANL01FL == "Y", PARAMCD == "ALT", AVISIT != "SCREENING")
#' adlb$AVISIT <- droplevels(adlb$AVISIT)
#' adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min))
#'
#' # Mean with CI
#' g_lineplot(adlb, adsl, subtitle = "Laboratory Test:")
#'
#' # Mean with CI, no stratification with group_var
#' g_lineplot(adlb, variables = control_lineplot_vars(group_var = NA))
#'
#' # Mean, upper whisker of CI, no group_var(strata) counts N
#' g_lineplot(
#'   adlb,
#'   whiskers = "mean_ci_upr",
#'   title = "Plot of Mean and Upper 95% Confidence Limit by Visit"
#' )
#'
#' # Median with CI
#' g_lineplot(
#'   adlb,
#'   adsl,
#'   mid = "median",
#'   interval = "median_ci",
#'   whiskers = c("median_ci_lwr", "median_ci_upr"),
#'   title = "Plot of Median and 95% Confidence Limits by Visit"
#' )
#'
#' # Mean, +/- SD
#' g_lineplot(adlb, adsl,
#'   interval = "mean_sdi",
#'   whiskers = c("mean_sdi_lwr", "mean_sdi_upr"),
#'   title = "Plot of Median +/- SD by Visit"
#' )
#'
#' # Mean with CI plot with stats table
#' g_lineplot(adlb, adsl, table = c("n", "mean", "mean_ci"))
#'
#' # Mean with CI, table and customized confidence level
#' g_lineplot(
#'   adlb,
#'   adsl,
#'   table = c("n", "mean", "mean_ci"),
#'   control = control_analyze_vars(conf_level = 0.80),
#'   title = "Plot of Mean and 80% Confidence Limits by Visit"
#' )
#'
#' # Mean with CI, table, filtered data
#' adlb_f <- dplyr::filter(adlb, ARMCD != "ARM A" | AVISIT == "BASELINE")
#' g_lineplot(adlb_f, table = c("n", "mean"))
#'
#' @export
g_lineplot <- function(df,
                       alt_counts_df = NULL,
                       variables = control_lineplot_vars(),
                       mid = "mean",
                       interval = "mean_ci",
                       whiskers = c("mean_ci_lwr", "mean_ci_upr"),
                       table = NULL,
                       sfun = s_summary,
                       ...,
                       mid_type = "pl",
                       mid_point_size = 2,
                       position = ggplot2::position_dodge(width = 0.4),
                       legend_title = NULL,
                       legend_position = "bottom",
                       ggtheme = nestcolor::theme_nest(),
                       x_lab = obj_label(df[[variables[["x"]]]]),
                       y_lab = NULL,
                       y_lab_add_paramcd = TRUE,
                       y_lab_add_unit = TRUE,
                       title = "Plot of Mean and 95% Confidence Limits by Visit",
                       subtitle = "",
                       subtitle_add_paramcd = TRUE,
                       subtitle_add_unit = TRUE,
                       caption = NULL,
                       table_format = summary_formats(),
                       table_labels = summary_labels(),
                       table_font_size = 3,
                       newpage = TRUE,
                       col = NULL) {
  checkmate::assert_character(variables, any.missing = TRUE)
  checkmate::assert_character(mid, null.ok = TRUE)
  checkmate::assert_character(interval, null.ok = TRUE)
  checkmate::assert_character(col, null.ok = TRUE)

  checkmate::assert_string(title, null.ok = TRUE)
  checkmate::assert_string(subtitle, null.ok = TRUE)

  if (is.character(interval)) {
    checkmate::assert_vector(whiskers, min.len = 0, max.len = 2)
  }

  if (length(whiskers) == 1) {
    checkmate::assert_character(mid)
  }

  if (is.character(mid)) {
    checkmate::assert_scalar(mid_type)
    checkmate::assert_subset(mid_type, c("pl", "p", "l"))
  }

  x <- variables[["x"]]
  y <- variables[["y"]]
  paramcd <- variables["paramcd"] # NA if paramcd == NA or it is not in variables
  y_unit <- variables["y_unit"] # NA if y_unit == NA or it is not in variables
  if (is.na(variables["group_var"])) {
    group_var <- NULL # NULL if group_var == NA or it is not in variables
  } else {
    group_var <- variables[["group_var"]]
    subject_var <- variables[["subject_var"]]
  }
  checkmate::assert_flag(y_lab_add_paramcd, null.ok = TRUE)
  checkmate::assert_flag(subtitle_add_paramcd, null.ok = TRUE)
  if ((!is.null(y_lab) && y_lab_add_paramcd) || (!is.null(subtitle) && subtitle_add_paramcd)) {
    checkmate::assert_false(is.na(paramcd))
    checkmate::assert_scalar(unique(df[[paramcd]]))
  }

  checkmate::assert_flag(y_lab_add_unit, null.ok = TRUE)
  checkmate::assert_flag(subtitle_add_unit, null.ok = TRUE)
  if ((!is.null(y_lab) && y_lab_add_unit) || (!is.null(subtitle) && subtitle_add_unit)) {
    checkmate::assert_false(is.na(y_unit))
    checkmate::assert_scalar(unique(df[[y_unit]]))
  }

  if (!is.null(group_var) && !is.null(alt_counts_df)) {
    checkmate::assert_set_equal(unique(alt_counts_df[[group_var]]), unique(df[[group_var]]))
  }

  ####################################### |
  # ---- Compute required statistics ----
  ####################################### |
  if (!is.null(group_var)) {
    df_grp <- tidyr::expand(df, .data[[group_var]], .data[[x]]) # expand based on levels of factors
  } else {
    df_grp <- tidyr::expand(df, NULL, .data[[x]])
  }
  df_grp <- df_grp %>%
    dplyr::full_join(y = df[, c(group_var, x, y)], by = c(group_var, x), multiple = "all") %>%
    dplyr::group_by_at(c(group_var, x))

  df_stats <- df_grp %>%
    dplyr::summarise(
      data.frame(t(do.call(c, unname(sfun(.data[[y]], ...)[c(mid, interval)])))),
      .groups = "drop"
    )

  df_stats <- df_stats[!is.na(df_stats[[mid]]), ]

  # add number of objects N in group_var (strata)
  if (!is.null(group_var) && !is.null(alt_counts_df)) {
    strata_N <- paste0(group_var, "_N") # nolint

    df_N <- stats::aggregate(eval(parse(text = subject_var)) ~ eval(parse(text = group_var)), data = alt_counts_df, FUN = function(x) length(unique(x))) # nolint
    colnames(df_N) <- c(group_var, "N") # nolint
    df_N[[strata_N]] <- paste0(df_N[[group_var]], " (N = ", df_N$N, ")") # nolint

    # strata_N should not be in clonames(df_stats)
    checkmate::assert_disjunct(strata_N, colnames(df_stats))

    df_stats <- merge(x = df_stats, y = df_N[, c(group_var, strata_N)], by = group_var)
  } else if (!is.null(group_var)) {
    strata_N <- group_var # nolint
  } else {
    strata_N <- NULL # nolint
  }

  ############################################### |
  # ---- Prepare certain plot's properties. ----
  ############################################### |
  # legend title
  if (is.null(legend_title) && !is.null(group_var) && legend_position != "none") {
    legend_title <- attr(df[[group_var]], "label")
  }

  # y label
  if (!is.null(y_lab)) {
    if (y_lab_add_paramcd) {
      y_lab <- paste(y_lab, unique(df[[paramcd]]))
    }

    if (y_lab_add_unit) {
      y_lab <- paste0(y_lab, " (", unique(df[[y_unit]]), ")")
    }

    y_lab <- trimws(y_lab)
  }

  # subtitle
  if (!is.null(subtitle)) {
    if (subtitle_add_paramcd) {
      subtitle <- paste(subtitle, unique(df[[paramcd]]))
    }

    if (subtitle_add_unit) {
      subtitle <- paste0(subtitle, " (", unique(df[[y_unit]]), ")")
    }

    subtitle <- trimws(subtitle)
  }

  ############################### |
  # ---- Build plot object. ----
  ############################### |
  p <- ggplot2::ggplot(
    data = df_stats,
    mapping = ggplot2::aes(
      x = .data[[x]], y = .data[[mid]],
      color = if (is.null(strata_N)) NULL else .data[[strata_N]],
      shape = if (is.null(strata_N)) NULL else .data[[strata_N]],
      lty = if (is.null(strata_N)) NULL else .data[[strata_N]],
      group = if (is.null(strata_N)) NULL else .data[[strata_N]]
    )
  )

  if (!is.null(mid)) {
    # points
    if (grepl("p", mid_type, fixed = TRUE)) {
      p <- p + ggplot2::geom_point(position = position, size = mid_point_size, na.rm = TRUE)
    }

    # lines
    # further conditions in if are to ensure that not all of the groups consist of only one observation
    if (grepl("l", mid_type, fixed = TRUE) && !is.null(group_var) &&
      !all(dplyr::summarise(df_grp, count_n = dplyr::n())[["count_n"]] == 1L)) { # nolint
      p <- p + ggplot2::geom_line(position = position, na.rm = TRUE)
    }
  }

  # interval
  if (!is.null(interval)) {
    p <- p +
      ggplot2::geom_errorbar(
        ggplot2::aes(ymin = .data[[whiskers[1]]], ymax = .data[[whiskers[max(1, length(whiskers))]]]),
        width = 0.45,
        position = position
      )

    if (length(whiskers) == 1) { # lwr or upr only; mid is then required
      # workaround as geom_errorbar does not provide single-direction whiskers
      p <- p +
        ggplot2::geom_linerange(
          data = df_stats[!is.na(df_stats[[whiskers]]), ], # as na.rm =TRUE does not suppress warnings
          ggplot2::aes(ymin = .data[[mid]], ymax = .data[[whiskers]]),
          position = position,
          na.rm = TRUE,
          show.legend = FALSE
        )
    }
  }

  p <- p +
    ggplot2::scale_y_continuous(labels = scales::comma) +
    ggplot2::labs(
      title = title,
      subtitle = subtitle,
      caption = caption,
      color = legend_title,
      lty = legend_title,
      shape = legend_title,
      x = x_lab,
      y = y_lab
    )

  if (!is.null(col)) {
    p <- p +
      ggplot2::scale_color_manual(values = col)
  }

  if (!is.null(ggtheme)) {
    p <- p + ggtheme
  } else {
    p <- p +
      ggplot2::theme_bw() +
      ggplot2::theme(
        legend.key.width = grid::unit(1, "cm"),
        legend.position = legend_position,
        legend.direction = ifelse(
          legend_position %in% c("top", "bottom"),
          "horizontal",
          "vertical"
        )
      )
  }

  ############################################################# |
  # ---- Optionally, add table to the bottom of the plot. ----
  ############################################################# |
  if (!is.null(table)) {
    df_stats_table <- df_grp %>%
      dplyr::summarise(
        h_format_row(
          x = sfun(.data[[y]], ...)[table],
          format = table_format,
          labels = table_labels
        ),
        .groups = "drop"
      )

    stats_lev <- rev(setdiff(colnames(df_stats_table), c(group_var, x)))

    df_stats_table <- df_stats_table %>%
      tidyr::pivot_longer(
        cols = -dplyr::all_of(c(group_var, x)),
        names_to = "stat",
        values_to = "value",
        names_ptypes = list(stat = factor(levels = stats_lev))
      )

    tbl <- ggplot2::ggplot(
      df_stats_table,
      ggplot2::aes(x = .data[[x]], y = .data[["stat"]], label = .data[["value"]])
    ) +
      ggplot2::geom_text(size = table_font_size) +
      ggplot2::theme_bw() +
      ggplot2::theme(
        panel.border = ggplot2::element_blank(),
        panel.grid.major = ggplot2::element_blank(),
        panel.grid.minor = ggplot2::element_blank(),
        axis.ticks = ggplot2::element_blank(),
        axis.title = ggplot2::element_blank(),
        axis.text.x = ggplot2::element_blank(),
        axis.text.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 5)),
        strip.text = ggplot2::element_text(hjust = 0),
        strip.text.x = ggplot2::element_text(margin = ggplot2::margin(1.5, 0, 1.5, 0, "pt")),
        strip.background = ggplot2::element_rect(fill = "grey95", color = NA),
        legend.position = "none"
      )

    if (!is.null(group_var)) {
      tbl <- tbl + ggplot2::facet_wrap(facets = group_var, ncol = 1)
    }

    # align plot and table
    cowplot::plot_grid(p, tbl, ncol = 1, align = "v", axis = "tblr")
  } else {
    p
  }
}

#' Helper function to get the right formatting in the optional table in `g_lineplot`.
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param x (named `list`)\cr list of numerical values to be formatted and optionally labeled.
#'   Elements of `x` must be `numeric` vectors.
#' @param format (named `character` or `NULL`)\cr format patterns for `x`. Names of the `format` must
#'   match the names of `x`. This parameter is passed directly to the `rtables::format_rcell`
#'   function through the `format` parameter.
#' @param labels (named `character` or `NULL`)\cr optional labels for `x`. Names of the `labels` must
#'   match the names of `x`. When a label is not specified for an element of `x`,
#'   then this function tries to use `label` or `names` (in this order) attribute of that element
#'   (depending on which one exists and it is not `NULL` or `NA` or `NaN`). If none of these attributes
#'   are attached to a given element of `x`, then the label is automatically generated.
#'
#' @return A single row `data.frame` object.
#'
#' @examples
#' mean_ci <- c(48, 51)
#' x <- list(mean = 50, mean_ci = mean_ci)
#' format <- c(mean = "xx.x", mean_ci = "(xx.xx, xx.xx)")
#' labels <- c(mean = "My Mean")
#' h_format_row(x, format, labels)
#'
#' attr(mean_ci, "label") <- "Mean 95% CI"
#' x <- list(mean = 50, mean_ci = mean_ci)
#' h_format_row(x, format, labels)
#'
#' @export
h_format_row <- function(x, format, labels = NULL) {
  # cell: one row, one column data.frame
  format_cell <- function(x, format, label = NULL) {
    fc <- format_rcell(x = x, format = unlist(format))
    if (is.na(fc)) {
      fc <- "NA"
    }
    x_label <- attr(x, "label")
    if (!is.null(label) && !is.na(label)) {
      names(fc) <- label
    } else if (!is.null(x_label) && !is.na(x_label)) {
      names(fc) <- x_label
    } else if (length(x) == length(fc)) {
      names(fc) <- names(x)
    }
    as.data.frame(t(fc))
  }

  row <- do.call(
    cbind,
    lapply(
      names(x), function(xn) format_cell(x[[xn]], format = format[xn], label = labels[xn])
    )
  )

  row
}

#' Control Function for `g_lineplot` Function
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Default values for `variables` parameter in `g_lineplot` function.
#' A variable's default value can be overwritten for any variable.
#'
#' @param x (`character`)\cr x variable name.
#' @param y (`character`)\cr y variable name.
#' @param group_var (`character` or `NA`)\cr group variable name.
#' @param strata (`character` or `NA`)\cr deprecated - group variable name.
#' @param subject_var (`character` or `NA`)\cr subject variable name.
#' @param cohort_id (`character` or `NA`)\cr deprecated - subject variable name.
#' @param paramcd (`character` or `NA`)\cr `paramcd` variable name.
#' @param y_unit (`character` or `NA`)\cr `y_unit` variable name.
#'
#' @return A named character vector of variable names.
#'
#' @examples
#' control_lineplot_vars()
#' control_lineplot_vars(group_var = NA)
#'
#' @export
control_lineplot_vars <- function(x = "AVISIT", y = "AVAL", group_var = "ARM", paramcd = "PARAMCD", y_unit = "AVALU",
                                  subject_var = "USUBJID", strata = lifecycle::deprecated(),
                                  cohort_id = lifecycle::deprecated()) {
  if (lifecycle::is_present(strata)) {
    lifecycle::deprecate_warn("0.9.2", "control_lineplot_vars(strata)", "control_lineplot_vars(group_var)")
    group_var <- strata
  }

  if (lifecycle::is_present(cohort_id)) {
    lifecycle::deprecate_warn("0.9.2", "control_lineplot_vars(cohort_id)", "control_lineplot_vars(subject_id)")
    subject_id <- cohort_id
  }

  checkmate::assert_string(x)
  checkmate::assert_string(y)
  checkmate::assert_string(group_var, na.ok = TRUE)
  checkmate::assert_string(subject_var, na.ok = TRUE)
  checkmate::assert_string(paramcd, na.ok = TRUE)
  checkmate::assert_string(y_unit, na.ok = TRUE)

  variables <- c(x = x, y = y, group_var = group_var, paramcd = paramcd, y_unit = y_unit, subject_var = subject_var)
  return(variables)
}

#' Convert List of Groups to Data Frame
#'
#' This converts a list of group levels into a data frame format which is expected by [rtables::add_combo_levels()].
#'
#' @param groups_list (named `list` of `character`)\cr specifies the new group levels via the names and the
#'   levels that belong to it in the character vectors that are elements of the list.
#'
#' @return [tibble::tibble()] in the required format.
#'
#' @examples
#' grade_groups <- list(
#'   "Any Grade (%)" = c("1", "2", "3", "4", "5"),
#'   "Grade 3-4 (%)" = c("3", "4"),
#'   "Grade 5 (%)" = "5"
#' )
#' groups_list_to_df(grade_groups)
#'
#' @export
groups_list_to_df <- function(groups_list) {
  checkmate::assert_list(groups_list, names = "named")
  lapply(groups_list, checkmate::assert_character)
  tibble::tibble(
    valname = make_names(names(groups_list)),
    label = names(groups_list),
    levelcombo = unname(groups_list),
    exargs = replicate(length(groups_list), list())
  )
}

#' Reference and Treatment Group Combination
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Facilitate the re-combination of groups divided as reference and treatment groups; it helps in arranging groups of
#' columns in the `rtables` framework and teal modules.
#'
#' @param fct (`factor`)\cr the variable with levels which needs to be grouped.
#' @param ref (`string`)\cr the reference level(s).
#' @param collapse (`string`)\cr a character string to separate `fct` and `ref`.
#'
#' @return A `list` with first item `ref` (reference) and second item `trt` (treatment).
#'
#' @examples
#' groups <- combine_groups(
#'   fct = DM$ARM,
#'   ref = c("B: Placebo")
#' )
#'
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze_vars("AGE") %>%
#'   build_table(DM)
#'
#' @export
combine_groups <- function(fct,
                           ref = NULL,
                           collapse = "/") {
  checkmate::assert_string(collapse)
  checkmate::assert_character(ref, min.chars = 1, any.missing = FALSE, null.ok = TRUE)
  checkmate::assert_multi_class(fct, classes = c("factor", "character"))

  fct <- as_factor_keep_attributes(fct)

  group_levels <- levels(fct)
  if (is.null(ref)) {
    ref <- group_levels[1]
  } else {
    checkmate::assert_subset(ref, group_levels)
  }

  groups <- list(
    ref = group_levels[group_levels %in% ref],
    trt = group_levels[!group_levels %in% ref]
  )
  stats::setNames(groups, nm = lapply(groups, paste, collapse = collapse))
}

#' Split Columns by Groups of Levels
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @inheritParams groups_list_to_df
#' @param ... additional arguments to [rtables::split_cols_by()] in order. For instance, to
#'   control formats (`format`), add a joint column for all groups (`incl_all`).
#'
#' @return A layout object suitable for passing to further layouting functions. Adding
#'   this function to an `rtable` layout will add a column split including the given
#'   groups to the table layout.
#'
#' @seealso [rtables::split_cols_by()]
#'
#' @examples
#' # 1 - Basic use
#'
#' # Without group combination `split_cols_by_groups` is
#' # equivalent to [rtables::split_cols_by()].
#' basic_table() %>%
#'   split_cols_by_groups("ARM") %>%
#'   add_colcounts() %>%
#'   analyze("AGE") %>%
#'   build_table(DM)
#'
#' # Add a reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", ref_group = "B: Placebo") %>%
#'   add_colcounts() %>%
#'   analyze(
#'     "AGE",
#'     afun = function(x, .ref_group, .in_ref_col) {
#'       if (.in_ref_col) {
#'         in_rows("Diff Mean" = rcell(NULL))
#'       } else {
#'         in_rows("Diff Mean" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#'       }
#'     }
#'   ) %>%
#'   build_table(DM)
#'
#' # 2 - Adding group specification
#'
#' # Manual preparation of the groups.
#' groups <- list(
#'   "Arms A+B" = c("A: Drug X", "B: Placebo"),
#'   "Arms A+C" = c("A: Drug X", "C: Combination")
#' )
#'
#' # Use of split_cols_by_groups without reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze("AGE") %>%
#'   build_table(DM)
#'
#' # Including differentiated output in the reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups_list = groups, ref_group = "Arms A+B") %>%
#'   analyze(
#'     "AGE",
#'     afun = function(x, .ref_group, .in_ref_col) {
#'       if (.in_ref_col) {
#'         in_rows("Diff. of Averages" = rcell(NULL))
#'       } else {
#'         in_rows("Diff. of Averages" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#'       }
#'     }
#'   ) %>%
#'   build_table(DM)
#'
#' # 3 - Binary list dividing factor levels into reference and treatment
#'
#' # `combine_groups` defines reference and treatment.
#' groups <- combine_groups(
#'   fct = DM$ARM,
#'   ref = c("A: Drug X", "B: Placebo")
#' )
#' groups
#'
#' # Use group definition without reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups_list = groups) %>%
#'   add_colcounts() %>%
#'   analyze("AGE") %>%
#'   build_table(DM)
#'
#' # Use group definition with reference column (first item of groups).
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups, ref_group = names(groups)[1]) %>%
#'   add_colcounts() %>%
#'   analyze(
#'     "AGE",
#'     afun = function(x, .ref_group, .in_ref_col) {
#'       if (.in_ref_col) {
#'         in_rows("Diff Mean" = rcell(NULL))
#'       } else {
#'         in_rows("Diff Mean" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#'       }
#'     }
#'   ) %>%
#'   build_table(DM)
#'
#' @export
split_cols_by_groups <- function(lyt,
                                 var,
                                 groups_list = NULL,
                                 ref_group = NULL,
                                 ...) {
  if (is.null(groups_list)) {
    split_cols_by(
      lyt = lyt,
      var = var,
      ref_group = ref_group,
      ...
    )
  } else {
    groups_df <- groups_list_to_df(groups_list)
    if (!is.null(ref_group)) {
      ref_group <- groups_df$valname[groups_df$label == ref_group]
    }
    split_cols_by(
      lyt = lyt,
      var = var,
      split_fun = add_combo_levels(groups_df, keep_levels = groups_df$valname),
      ref_group = ref_group,
      ...
    )
  }
}

#' Combine Counts
#'
#' Simplifies the estimation of column counts, especially when group combination is required.
#'
#' @inheritParams combine_groups
#' @inheritParams groups_list_to_df
#'
#' @return A `vector` of column counts.
#'
#' @seealso [combine_groups()]
#'
#' @examples
#' ref <- c("A: Drug X", "B: Placebo")
#' groups <- combine_groups(fct = DM$ARM, ref = ref)
#'
#' col_counts <- combine_counts(
#'   fct = DM$ARM,
#'   groups_list = groups
#' )
#'
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze_vars("AGE") %>%
#'   build_table(DM, col_counts = col_counts)
#'
#' ref <- "A: Drug X"
#' groups <- combine_groups(fct = DM$ARM, ref = ref)
#' col_counts <- combine_counts(
#'   fct = DM$ARM,
#'   groups_list = groups
#' )
#'
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze_vars("AGE") %>%
#'   build_table(DM, col_counts = col_counts)
#'
#' @export
combine_counts <- function(fct, groups_list = NULL) {
  checkmate::assert_multi_class(fct, classes = c("factor", "character"))

  fct <- as_factor_keep_attributes(fct)

  if (is.null(groups_list)) {
    y <- table(fct)
    y <- stats::setNames(as.numeric(y), nm = dimnames(y)[[1]])
  } else {
    y <- vapply(
      X = groups_list,
      FUN = function(x) sum(table(fct)[x]),
      FUN.VALUE = 1
    )
  }
  y
}

#' Subgroup Treatment Effect Pattern (STEP) Fit for Survival Outcome
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This fits the Subgroup Treatment Effect Pattern models for a survival outcome. The treatment arm
#' variable must have exactly 2 levels, where the first one is taken as reference and the estimated
#' hazard ratios are for the comparison of the second level vs. the first one.
#'
#' The model which is fit is:
#'
#' `Surv(time, event) ~ arm * poly(biomarker, degree) + covariates + strata(strata)`
#'
#' where `degree` is specified by `control_step()`.
#'
#' @inheritParams argument_convention
#' @param variables (named `list` of `character`)\cr list of analysis variables: needs `time`, `event`,
#'   `arm`, `biomarker`, and optional `covariates` and `strata`.
#' @param control (named `list`)\cr combined control list from [control_step()] and [control_coxph()].
#'
#' @return A matrix of class `step`. The first part of the columns describe the subgroup intervals used
#'   for the biomarker variable, including where the center of the intervals are and their bounds. The
#'   second part of the columns contain the estimates for the treatment arm comparison.
#'
#' @note For the default degree 0 the `biomarker` variable is not included in the model.
#'
#' @seealso [control_step()] and [control_coxph()] for the available customization options.
#'
#' @examples
#' # Testing dataset with just two treatment arms.
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("ARM" = "Treatment Arm", "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' variables <- list(
#'   arm = "ARM",
#'   biomarker = "BMRKR1",
#'   covariates = c("AGE", "BMRKR2"),
#'   event = "is_event",
#'   time = "AVAL"
#' )
#'
#' # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup.
#' step_matrix <- fit_survival_step(
#'   variables = variables,
#'   data = adtte_f
#' )
#' dim(step_matrix)
#' head(step_matrix)
#'
#' # Specify different polynomial degree for the biomarker interaction to use more flexible local
#' # models. Or specify different Cox regression options.
#' step_matrix2 <- fit_survival_step(
#'   variables = variables,
#'   data = adtte_f,
#'   control = c(control_coxph(conf_level = 0.9), control_step(degree = 2))
#' )
#'
#' # Use a global model with cubic interaction and only 5 points.
#' step_matrix3 <- fit_survival_step(
#'   variables = variables,
#'   data = adtte_f,
#'   control = c(control_coxph(), control_step(bandwidth = NULL, degree = 3, num_points = 5L))
#' )
#'
#' @export
fit_survival_step <- function(variables,
                              data,
                              control = c(control_step(), control_coxph())) {
  checkmate::assert_list(control)
  assert_df_with_variables(data, variables)
  data <- data[!is.na(data[[variables$biomarker]]), ]
  window_sel <- h_step_window(x = data[[variables$biomarker]], control = control)
  interval_center <- window_sel$interval[, "Interval Center"]
  form <- h_step_survival_formula(variables = variables, control = control)
  estimates <- if (is.null(control$bandwidth)) {
    h_step_survival_est(
      formula = form,
      data = data,
      variables = variables,
      x = interval_center,
      control = control
    )
  } else {
    tmp <- mapply(
      FUN = h_step_survival_est,
      x = interval_center,
      subset = as.list(as.data.frame(window_sel$sel)),
      MoreArgs = list(
        formula = form,
        data = data,
        variables = variables,
        control = control
      )
    )
    # Maybe we find a more elegant solution than this.
    rownames(tmp) <- c("n", "events", "loghr", "se", "ci_lower", "ci_upper")
    t(tmp)
  }
  result <- cbind(window_sel$interval, estimates)
  structure(
    result,
    class = c("step", "matrix"),
    variables = variables,
    control = control
  )
}

#' Missing Data
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Substitute missing data with a string or factor level.
#'
#' @param x (`factor` or `character` vector)\cr values for which any missing values should be substituted.
#' @param label (`character`)\cr string that missing data should be replaced with.
#'
#' @return `x` with any `NA` values substituted by `label`.
#'
#' @examples
#' explicit_na(c(NA, "a", "b"))
#' is.na(explicit_na(c(NA, "a", "b")))
#'
#' explicit_na(factor(c(NA, "a", "b")))
#' is.na(explicit_na(factor(c(NA, "a", "b"))))
#'
#' explicit_na(sas_na(c("a", "")))
#'
#' @export
explicit_na <- function(x, label = "<Missing>") {
  checkmate::assert_string(label)

  if (is.factor(x)) {
    x <- forcats::fct_na_value_to_level(x, label)
    forcats::fct_drop(x, only = label)
  } else if (is.character(x)) {
    x[is.na(x)] <- label
    x
  } else {
    stop("only factors and character vectors allowed")
  }
}

#' Convert Strings to `NA`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' SAS imports missing data as empty strings or strings with whitespaces only. This helper function can be used to
#' convert these values to `NA`s.
#'
#' @inheritParams explicit_na
#' @param empty (`logical`)\cr if `TRUE` empty strings get replaced by `NA`.
#' @param whitespaces (`logical`)\cr if `TRUE` then strings made from whitespaces only get replaced with `NA`.
#'
#' @return `x` with `""` and/or whitespace-only values substituted by `NA`, depending on the values of
#'   `empty` and `whitespaces`.
#'
#' @examples
#' sas_na(c("1", "", " ", "   ", "b"))
#' sas_na(factor(c("", " ", "b")))
#'
#' is.na(sas_na(c("1", "", " ", "   ", "b")))
#'
#' @export
sas_na <- function(x, empty = TRUE, whitespaces = TRUE) {
  checkmate::assert_flag(empty)
  checkmate::assert_flag(whitespaces)

  if (is.factor(x)) {
    empty_levels <- levels(x) == ""
    if (empty && any(empty_levels)) levels(x)[empty_levels] <- NA

    ws_levels <- grepl("^\\s+$", levels(x))
    if (whitespaces && any(ws_levels)) levels(x)[ws_levels] <- NA

    x
  } else if (is.character(x)) {
    if (empty) x[x == ""] <- NA_character_

    if (whitespaces) x[grepl("^\\s+$", x)] <- NA_character_

    x
  } else {
    stop("only factors and character vectors allowed")
  }
}

#' Incidence Rate
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Estimate the event rate adjusted for person-years at risk, otherwise known
#' as incidence rate. Primary analysis variable is the person-years at risk.
#'
#' @inheritParams argument_convention
#' @param control (`list`)\cr parameters for estimation details, specified by using
#'   the helper function [control_incidence_rate()]. Possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level for the estimated incidence rate.
#'   * `conf_type` (`string`)\cr `normal` (default), `normal_log`, `exact`, or `byar`
#'     for confidence interval type.
#'   * `input_time_unit` (`string`)\cr `day`, `week`, `month`, or `year` (default)
#'     indicating time unit for data input.
#'   * `num_pt_year` (`numeric`)\cr time unit for desired output (in person-years).
#' @param n_events (`integer`)\cr number of events observed.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_incidence_rate")`
#'   to see available statistics for this function.
#'
#' @seealso [control_incidence_rate()] and helper functions [h_incidence_rate].
#'
#' @name incidence_rate
#' @order 1
NULL

#' @describeIn incidence_rate Statistics function which estimates the incidence rate and the
#'   associated confidence interval.
#'
#' @return
#' * `s_incidence_rate()` returns the following statistics:
#'   - `person_years`: Total person-years at risk.
#'   - `n_events`: Total number of events observed.
#'   - `rate`: Estimated incidence rate.
#'   - `rate_ci`: Confidence interval for the incidence rate.
#'
#' @keywords internal
s_incidence_rate <- function(df,
                             .var,
                             n_events,
                             is_event,
                             control = control_incidence_rate()) {
  if (!missing(is_event)) {
    warning("argument is_event will be deprecated. Please use n_events.")

    if (missing(n_events)) {
      assert_df_with_variables(df, list(tte = .var, is_event = is_event))
      checkmate::assert_string(.var)
      checkmate::assert_logical(df[[is_event]], any.missing = FALSE)
      checkmate::assert_numeric(df[[.var]], any.missing = FALSE)
      n_events <- is_event
    }
  } else {
    assert_df_with_variables(df, list(tte = .var, n_events = n_events))
    checkmate::assert_string(.var)
    checkmate::assert_numeric(df[[.var]], any.missing = FALSE)
    checkmate::assert_integer(df[[n_events]], any.missing = FALSE)
  }

  input_time_unit <- control$input_time_unit
  num_pt_year <- control$num_pt_year
  conf_level <- control$conf_level
  person_years <- sum(df[[.var]], na.rm = TRUE) * (
    1 * (input_time_unit == "year") +
      1 / 12 * (input_time_unit == "month") +
      1 / 52.14 * (input_time_unit == "week") +
      1 / 365.24 * (input_time_unit == "day")
  )
  n_events <- sum(df[[n_events]], na.rm = TRUE)

  result <- h_incidence_rate(
    person_years,
    n_events,
    control
  )
  list(
    person_years = formatters::with_label(person_years, "Total patient-years at risk"),
    n_events = formatters::with_label(n_events, "Number of adverse events observed"),
    rate = formatters::with_label(result$rate, paste("AE rate per", num_pt_year, "patient-years")),
    rate_ci = formatters::with_label(result$rate_ci, f_conf_level(conf_level))
  )
}

#' @describeIn incidence_rate Formatted analysis function which is used as `afun`
#'   in `estimate_incidence_rate()`.
#'
#' @return
#' * `a_incidence_rate()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_incidence_rate <- make_afun(
  s_incidence_rate,
  .formats = c(
    "person_years" = "xx.x",
    "n_events" = "xx",
    "rate" = "xx.xx",
    "rate_ci" = "(xx.xx, xx.xx)"
  )
)

#' @describeIn incidence_rate Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `estimate_incidence_rate()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_incidence_rate()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' df <- data.frame(
#'   USUBJID = as.character(seq(6)),
#'   CNSR = c(0, 1, 1, 0, 0, 0),
#'   AVAL = c(10.1, 20.4, 15.3, 20.8, 18.7, 23.4),
#'   ARM = factor(c("A", "A", "A", "B", "B", "B"))
#' ) %>%
#'   mutate(is_event = CNSR == 0) %>%
#'   mutate(n_events = as.integer(is_event))
#'
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   estimate_incidence_rate(
#'     vars = "AVAL",
#'     n_events = "n_events",
#'     control = control_incidence_rate(
#'       input_time_unit = "month",
#'       num_pt_year = 100
#'     )
#'   ) %>%
#'   build_table(df)
#'
#' @export
#' @order 2
estimate_incidence_rate <- function(lyt,
                                    vars,
                                    n_events,
                                    control = control_incidence_rate(),
                                    na_str = default_na_str(),
                                    nested = TRUE,
                                    ...,
                                    show_labels = "hidden",
                                    table_names = vars,
                                    .stats = NULL,
                                    .formats = NULL,
                                    .labels = NULL,
                                    .indent_mods = NULL) {
  extra_args <- list(n_events = n_events, control = control, ...)

  afun <- make_afun(
    a_incidence_rate,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    show_labels = show_labels,
    table_names = table_names,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Helper Functions for Incidence Rate
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param control (`list`)\cr parameters for estimation details, specified by using
#'   the helper function [control_incidence_rate()]. Possible parameter options are:
#'   * `conf_level`: (`proportion`)\cr confidence level for the estimated incidence rate.
#'   * `conf_type`: (`string`)\cr `normal` (default), `normal_log`, `exact`, or `byar`
#'     for confidence interval type.
#'   * `input_time_unit`: (`string`)\cr `day`, `week`, `month`, or `year` (default)
#'     indicating time unit for data input.
#'   * `num_pt_year`: (`numeric`)\cr time unit for desired output (in person-years).
#' @param person_years (`numeric`)\cr total person-years at risk.
#' @param alpha (`numeric`)\cr two-sided alpha-level for confidence interval.
#' @param n_events (`integer`)\cr number of events observed.
#'
#' @return Estimated incidence rate `rate` and associated confidence interval `rate_ci`.
#'
#' @seealso [incidence_rate]
#'
#' @name h_incidence_rate
NULL

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated confidence interval based on the normal approximation for the
#'   incidence rate. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_normal(200, 2)
#'
#' @export
h_incidence_rate_normal <- function(person_years,
                                    n_events,
                                    alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  est <- n_events / person_years
  se <- sqrt(est / person_years)
  ci <- est + c(-1, 1) * stats::qnorm(1 - alpha / 2) * se

  list(rate = est, rate_ci = ci)
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated confidence interval based on the normal approximation for the
#'   logarithm of the incidence rate. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_normal_log(200, 2)
#'
#' @export
h_incidence_rate_normal_log <- function(person_years,
                                        n_events,
                                        alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  rate_est <- n_events / person_years
  rate_se <- sqrt(rate_est / person_years)
  lrate_est <- log(rate_est)
  lrate_se <- rate_se / rate_est
  ci <- exp(lrate_est + c(-1, 1) * stats::qnorm(1 - alpha / 2) * lrate_se)

  list(rate = rate_est, rate_ci = ci)
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated exact confidence interval. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_exact(200, 2)
#'
#' @export
h_incidence_rate_exact <- function(person_years,
                                   n_events,
                                   alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  est <- n_events / person_years
  lcl <- stats::qchisq(p = (alpha) / 2, df = 2 * n_events) / (2 * person_years)
  ucl <- stats::qchisq(p = 1 - (alpha) / 2, df = 2 * n_events + 2) / (2 * person_years)

  list(rate = est, rate_ci = c(lcl, ucl))
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated `Byar`'s confidence interval. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_byar(200, 2)
#'
#' @export
h_incidence_rate_byar <- function(person_years,
                                  n_events,
                                  alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  est <- n_events / person_years
  seg_1 <- n_events + 0.5
  seg_2 <- 1 - 1 / (9 * (n_events + 0.5))
  seg_3 <- stats::qnorm(1 - alpha / 2) * sqrt(1 / (n_events + 0.5)) / 3
  lcl <- seg_1 * ((seg_2 - seg_3)^3) / person_years
  ucl <- seg_1 * ((seg_2 + seg_3) ^ 3) / person_years # styler: off

  list(rate = est, rate_ci = c(lcl, ucl))
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated confidence interval.
#'
#' @keywords internal
h_incidence_rate <- function(person_years,
                             n_events,
                             control = control_incidence_rate()) {
  alpha <- 1 - control$conf_level
  est <- switch(control$conf_type,
    normal = h_incidence_rate_normal(person_years, n_events, alpha),
    normal_log = h_incidence_rate_normal_log(person_years, n_events, alpha),
    exact = h_incidence_rate_exact(person_years, n_events, alpha),
    byar = h_incidence_rate_byar(person_years, n_events, alpha)
  )

  num_pt_year <- control$num_pt_year
  list(
    rate = est$rate * num_pt_year,
    rate_ci = est$rate_ci * num_pt_year
  )
}

#' Summarize Variables in Columns
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This analyze function uses the S3 generic function [s_summary()] to summarize different variables
#' that are arranged in columns. Additional standard formatting arguments are available. It is a
#' minimal wrapper for [rtables::analyze_colvars()]. The latter function is meant to add different
#' analysis methods for each column variables as different rows. To have the analysis methods as
#' column labels, please refer to [analyze_vars_in_cols()].
#'
#' @inheritParams argument_convention
#' @param ... arguments passed to `s_summary()`.
#' @param .indent_mods (named `vector` of `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @return
#' A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
#' Adding this function to an `rtable` layout will summarize the given variables, arrange the output
#' in columns, and add it to the table layout.
#'
#' @seealso [rtables::split_cols_by_multivar()] and [`analyze_colvars_functions`].
#'
#' @examples
#' dta_test <- data.frame(
#'   USUBJID = rep(1:6, each = 3),
#'   PARAMCD = rep("lab", 6 * 3),
#'   AVISIT = rep(paste0("V", 1:3), 6),
#'   ARM = rep(LETTERS[1:3], rep(6, 3)),
#'   AVAL = c(9:1, rep(NA, 9)),
#'   CHG = c(1:9, rep(NA, 9))
#' )
#'
#' ## Default output within a `rtables` pipeline.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
#'   summarize_colvars() %>%
#'   build_table(dta_test)
#'
#' ## Selection of statistics, formats and labels also work.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
#'   summarize_colvars(
#'     .stats = c("n", "mean_sd"),
#'     .formats = c("mean_sd" = "xx.x, xx.x"),
#'     .labels = c(n = "n", mean_sd = "Mean, SD")
#'   ) %>%
#'   build_table(dta_test)
#'
#' ## Use arguments interpreted by `s_summary`.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
#'   summarize_colvars(na.rm = FALSE) %>%
#'   build_table(dta_test)
#'
#' @export
summarize_colvars <- function(lyt,
                              ...,
                              na_str = default_na_str(),
                              .stats = c("n", "mean_sd", "median", "range", "count_fraction"),
                              .formats = NULL,
                              .labels = NULL,
                              .indent_mods = NULL) {
  extra_args <- list(.stats = .stats, na_str = na_str, ...)
  if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
  if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
  if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods

  analyze_colvars(
    lyt,
    afun = a_summary,
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Occurrence Counts by Grade
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions for analyzing frequencies and fractions of occurrences by grade for patients
#' with occurrence data. Multiple occurrences within one individual are counted once at the
#' greatest intensity/highest grade level.
#'
#' @inheritParams argument_convention
#' @param grade_groups (named `list` of `character`)\cr containing groupings of grades.
#' @param remove_single (`logical`)\cr `TRUE` to not include the elements of one-element grade groups
#'   in the the output list; in this case only the grade groups names will be included in the output. If
#'   `only_grade_groups` is set to `TRUE` this argument is ignored.
#' @param only_grade_groups (`flag`)\cr whether only the specified grade groups should be
#'   included, with individual grade rows removed (`TRUE`), or all grades and grade groups
#'   should be displayed (`FALSE`).
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_occurrences_by_grade")`
#'   to see available statistics for this function.
#'
#' @seealso Relevant helper function [h_append_grade_groups()].
#'
#' @name count_occurrences_by_grade
#' @order 1
NULL

#' Helper function for [s_count_occurrences_by_grade()]
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function for [s_count_occurrences_by_grade()] to insert grade groupings into list with
#' individual grade frequencies. The order of the final result follows the order of `grade_groups`.
#' The elements under any-grade group (if any), i.e. the grade group equal to `refs` will be moved to
#' the end. Grade groups names must be unique.
#'
#' @inheritParams count_occurrences_by_grade
#' @param refs (named `list` of `numeric`)\cr where each name corresponds to a reference grade level
#'   and each entry represents a count.
#'
#' @return Formatted list of grade groupings.
#'
#' @examples
#' h_append_grade_groups(
#'   list(
#'     "Any Grade" = as.character(1:5),
#'     "Grade 1-2" = c("1", "2"),
#'     "Grade 3-4" = c("3", "4")
#'   ),
#'   list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)
#' )
#'
#' h_append_grade_groups(
#'   list(
#'     "Any Grade" = as.character(5:1),
#'     "Grade A" = "5",
#'     "Grade B" = c("4", "3")
#'   ),
#'   list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)
#' )
#'
#' h_append_grade_groups(
#'   list(
#'     "Any Grade" = as.character(1:5),
#'     "Grade 1-2" = c("1", "2"),
#'     "Grade 3-4" = c("3", "4")
#'   ),
#'   list("1" = 10, "2" = 5, "3" = 0)
#' )
#'
#' @export
h_append_grade_groups <- function(grade_groups, refs, remove_single = TRUE, only_grade_groups = FALSE) {
  checkmate::assert_list(grade_groups)
  checkmate::assert_list(refs)
  refs_orig <- refs
  elements <- unique(unlist(grade_groups))

  ### compute sums in groups
  grp_sum <- lapply(grade_groups, function(i) do.call(sum, refs[i]))
  if (!checkmate::test_subset(elements, names(refs))) {
    padding_el <- setdiff(elements, names(refs))
    refs[padding_el] <- 0
  }
  result <- c(grp_sum, refs)

  ### order result while keeping grade_groups's ordering
  ordr <- grade_groups

  # elements of any-grade group (if any) will be moved to the end
  is_any <- sapply(grade_groups, setequal, y = names(refs))
  ordr[is_any] <- list(character(0)) # hide elements under any-grade group

  # groups-elements combined sequence
  ordr <- c(lapply(names(ordr), function(g) c(g, ordr[[g]])), recursive = TRUE, use.names = FALSE)
  ordr <- ordr[!duplicated(ordr)]

  # append remaining elements (if any)
  ordr <- union(ordr, unlist(grade_groups[is_any])) # from any-grade group
  ordr <- union(ordr, names(refs)) # from refs

  # remove elements of single-element groups, if any
  if (only_grade_groups) {
    ordr <- intersect(ordr, names(grade_groups))
  } else if (remove_single) {
    is_single <- sapply(grade_groups, length) == 1L
    ordr <- setdiff(ordr, unlist(grade_groups[is_single]))
  }

  # apply the order
  result <- result[ordr]

  # remove groups without any elements in the original refs
  # note: it's OK if groups have 0 value
  keep_grp <- vapply(grade_groups, function(x, rf) {
    any(x %in% rf)
  }, rf = names(refs_orig), logical(1))

  keep_el <- names(result) %in% names(refs_orig) | names(result) %in% names(keep_grp)[keep_grp]
  result <- result[keep_el]

  result
}

#' @describeIn count_occurrences_by_grade Statistics function which counts the
#'  number of patients by highest grade.
#'
#' @return
#' * `s_count_occurrences_by_grade()` returns a list of counts and fractions with one element per grade level or
#'   grade level grouping.
#'
#' @examples
#' s_count_occurrences_by_grade(
#'   df,
#'   .N_col = 10L,
#'   .var = "AETOXGR",
#'   id = "USUBJID",
#'   grade_groups = list("ANY" = levels(df$AETOXGR))
#' )
#'
#' @export
s_count_occurrences_by_grade <- function(df,
                                         .var,
                                         .N_col, # nolint
                                         id = "USUBJID",
                                         grade_groups = list(),
                                         remove_single = TRUE,
                                         only_grade_groups = FALSE,
                                         labelstr = "") {
  assert_valid_factor(df[[.var]])
  assert_df_with_variables(df, list(grade = .var, id = id))

  if (nrow(df) < 1) {
    grade_levels <- levels(df[[.var]])
    l_count <- as.list(rep(0, length(grade_levels)))
    names(l_count) <- grade_levels
  } else {
    if (isTRUE(is.factor(df[[id]]))) {
      assert_valid_factor(df[[id]], any.missing = FALSE)
    } else {
      checkmate::assert_character(df[[id]], min.chars = 1, any.missing = FALSE)
    }
    checkmate::assert_count(.N_col)

    id <- df[[id]]
    grade <- df[[.var]]

    if (!is.ordered(grade)) {
      grade_lbl <- obj_label(grade)
      lvls <- levels(grade)
      if (sum(grepl("^\\d+$", lvls)) %in% c(0, length(lvls))) {
        lvl_ord <- lvls
      } else {
        lvls[!grepl("^\\d+$", lvls)] <- min(as.numeric(lvls[grepl("^\\d+$", lvls)])) - 1
        lvl_ord <- levels(grade)[order(as.numeric(lvls))]
      }
      grade <- formatters::with_label(factor(grade, levels = lvl_ord, ordered = TRUE), grade_lbl)
    }

    missing_lvl <- grepl("missing", tolower(levels(grade)))
    if (any(missing_lvl)) {
      grade <- factor(
        grade,
        levels = c(levels(grade)[!missing_lvl], levels(grade)[missing_lvl]),
        ordered = is.ordered(grade)
      )
    }
    df_max <- stats::aggregate(grade ~ id, FUN = max, drop = FALSE)
    l_count <- as.list(table(df_max$grade))
  }

  if (length(grade_groups) > 0) {
    l_count <- h_append_grade_groups(grade_groups, l_count, remove_single, only_grade_groups)
  }

  l_count_fraction <- lapply(l_count, function(i, denom) c(i, i / denom), denom = .N_col)

  list(
    count_fraction = l_count_fraction
  )
}

#' @describeIn count_occurrences_by_grade Formatted analysis function which is used as `afun`
#'   in `count_occurrences_by_grade()`.
#'
#' @return
#' * `a_count_occurrences_by_grade()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' #  We need to ungroup `count_fraction` first so that the `rtables` formatting
#' # function `format_count_fraction()` can be applied correctly.
#' afun <- make_afun(a_count_occurrences_by_grade, .ungroup_stats = "count_fraction")
#' afun(
#'   df,
#'   .N_col = 10L,
#'   .var = "AETOXGR",
#'   id = "USUBJID",
#'   grade_groups = list("ANY" = levels(df$AETOXGR))
#' )
#'
#' @export
a_count_occurrences_by_grade <- make_afun(
  s_count_occurrences_by_grade,
  .formats = c("count_fraction" = format_count_fraction_fixed_dp)
)

#' @describeIn count_occurrences_by_grade Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_occurrences_by_grade()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(1:6, 1)),
#'   ARM = factor(c("A", "A", "A", "B", "B", "B", "A"), levels = c("A", "B")),
#'   AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)),
#'   AESEV = factor(
#'     x = c("MILD", "MODERATE", "SEVERE", "MILD", "MILD", "MODERATE", "SEVERE"),
#'     levels = c("MILD", "MODERATE", "SEVERE")
#'   ),
#'   stringsAsFactors = FALSE
#' )
#'
#' df_adsl <- df %>%
#'   select(USUBJID, ARM) %>%
#'   unique()
#'
#' # Layout creating function with custom format.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_occurrences_by_grade(
#'     var = "AESEV",
#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' # Define additional grade groupings.
#' grade_groups <- list(
#'   "-Any-" = c("1", "2", "3", "4", "5"),
#'   "Grade 1-2" = c("1", "2"),
#'   "Grade 3-5" = c("3", "4", "5")
#' )
#'
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_occurrences_by_grade(
#'     var = "AETOXGR",
#'     grade_groups = grade_groups,
#'     only_grade_groups = TRUE
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' @export
#' @order 2
count_occurrences_by_grade <- function(lyt,
                                       var,
                                       id = "USUBJID",
                                       grade_groups = list(),
                                       remove_single = TRUE,
                                       only_grade_groups = FALSE,
                                       var_labels = var,
                                       show_labels = "default",
                                       riskdiff = FALSE,
                                       na_str = default_na_str(),
                                       nested = TRUE,
                                       ...,
                                       table_names = var,
                                       .stats = NULL,
                                       .formats = NULL,
                                       .indent_mods = NULL,
                                       .labels = NULL) {
  checkmate::assert_flag(riskdiff)
  s_args <- list(
    id = id, grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups, ...
  )

  afun <- make_afun(
    a_count_occurrences_by_grade,
    .stats = .stats,
    .formats = .formats,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )

  extra_args <- if (isFALSE(riskdiff)) {
    s_args
  } else {
    list(
      afun = list("s_count_occurrences_by_grade" = afun),
      .stats = .stats,
      .indent_mods = .indent_mods,
      s_args = s_args
    )
  }

  analyze(
    lyt = lyt,
    vars = var,
    var_labels = var_labels,
    show_labels = show_labels,
    afun = ifelse(isFALSE(riskdiff), afun, afun_riskdiff),
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' @describeIn count_occurrences_by_grade Layout-creating function which can take content function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
#'   containing the statistics from `s_count_occurrences_by_grade()` to the table layout.
#'
#' @examples
#' # Layout creating function with custom format.
#' basic_table() %>%
#'   add_colcounts() %>%
#'   split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
#'   summarize_occurrences_by_grade(
#'     var = "AESEV",
#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' basic_table() %>%
#'   add_colcounts() %>%
#'   split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
#'   summarize_occurrences_by_grade(
#'     var = "AETOXGR",
#'     grade_groups = grade_groups
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' @export
#' @order 3
summarize_occurrences_by_grade <- function(lyt,
                                           var,
                                           id = "USUBJID",
                                           grade_groups = list(),
                                           remove_single = TRUE,
                                           only_grade_groups = FALSE,
                                           na_str = default_na_str(),
                                           ...,
                                           .stats = NULL,
                                           .formats = NULL,
                                           .indent_mods = NULL,
                                           .labels = NULL) {
  extra_args <- list(
    id = id, grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups, ...
  )

  cfun <- make_afun(
    a_count_occurrences_by_grade,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )

  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = cfun,
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Survival Time Point Analysis
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Summarize patients' survival rate and difference of survival rates between groups at a time point.
#'
#' @inheritParams argument_convention
#' @inheritParams s_surv_time
#' @param time_point (`number`)\cr survival time point of interest.
#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function
#'   [control_surv_timepoint()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for survival rate.
#'   * `conf_type` (`string`)\cr confidence interval type. Options are "plain" (default), "log", "log-log",
#'     see more in [survival::survfit()]. Note option "none" is no longer supported.
#'   * `time_point` (`number`)\cr survival time point of interest.
#' @param method (`string`)\cr either `surv` (survival estimations),
#'   `surv_diff` (difference in survival with the control) or `both`.
#' @param table_names_suffix (`string`)\cr optional suffix for the `table_names` used for the `rtables` to
#'   avoid warnings from duplicate table names.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("surv_timepoint")`
#'   to see available statistics for this function.
#' @param .indent_mods (named `vector` of `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @name survival_timepoint
#' @order 1
NULL

#' @describeIn survival_timepoint Statistics function which analyzes survival rate.
#'
#' @return
#' * `s_surv_timepoint()` returns the statistics:
#'   * `pt_at_risk`: Patients remaining at risk.
#'   * `event_free_rate`: Event-free rate (%).
#'   * `rate_se`: Standard error of event free rate.
#'   * `rate_ci`: Confidence interval for event free rate.
#'
#' @keywords internal
s_surv_timepoint <- function(df,
                             .var,
                             time_point,
                             is_event,
                             control = control_surv_timepoint()) {
  checkmate::assert_string(.var)
  assert_df_with_variables(df, list(tte = .var, is_event = is_event))
  checkmate::assert_numeric(df[[.var]], min.len = 1, any.missing = FALSE)
  checkmate::assert_number(time_point)
  checkmate::assert_logical(df[[is_event]], min.len = 1, any.missing = FALSE)

  conf_type <- control$conf_type
  conf_level <- control$conf_level

  formula <- stats::as.formula(paste0("survival::Surv(", .var, ", ", is_event, ") ~ 1"))
  srv_fit <- survival::survfit(
    formula = formula,
    data = df,
    conf.int = conf_level,
    conf.type = conf_type
  )
  s_srv_fit <- summary(srv_fit, times = time_point, extend = TRUE)
  df_srv_fit <- as.data.frame(s_srv_fit[c("time", "n.risk", "surv", "lower", "upper", "std.err")])
  if (df_srv_fit[["n.risk"]] == 0) {
    pt_at_risk <- event_free_rate <- rate_se <- NA_real_
    rate_ci <- c(NA_real_, NA_real_)
  } else {
    pt_at_risk <- df_srv_fit$n.risk
    event_free_rate <- df_srv_fit$surv
    rate_se <- df_srv_fit$std.err
    rate_ci <- c(df_srv_fit$lower, df_srv_fit$upper)
  }
  list(
    pt_at_risk = formatters::with_label(pt_at_risk, "Patients remaining at risk"),
    event_free_rate = formatters::with_label(event_free_rate * 100, "Event Free Rate (%)"),
    rate_se = formatters::with_label(rate_se * 100, "Standard Error of Event Free Rate"),
    rate_ci = formatters::with_label(rate_ci * 100, f_conf_level(conf_level))
  )
}

#' @describeIn survival_timepoint Formatted analysis function which is used as `afun` in `surv_timepoint()`
#'   when `method = "surv"`.
#'
#' @return
#' * `a_surv_timepoint()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_surv_timepoint <- make_afun(
  s_surv_timepoint,
  .indent_mods = c(
    pt_at_risk = 0L,
    event_free_rate = 0L,
    rate_se = 1L,
    rate_ci = 1L
  ),
  .formats = c(
    pt_at_risk = "xx",
    event_free_rate = "xx.xx",
    rate_se = "xx.xx",
    rate_ci = "(xx.xx, xx.xx)"
  )
)

#' @describeIn survival_timepoint Statistics function which analyzes difference between two survival rates.
#'
#' @return
#' * `s_surv_timepoint_diff()` returns the statistics:
#'   * `rate_diff`: Event-free rate difference between two groups.
#'   * `rate_diff_ci`: Confidence interval for the difference.
#'   * `ztest_pval`: p-value to test the difference is 0.
#'
#' @keywords internal
s_surv_timepoint_diff <- function(df,
                                  .var,
                                  .ref_group,
                                  .in_ref_col,
                                  time_point,
                                  control = control_surv_timepoint(),
                                  ...) {
  if (.in_ref_col) {
    return(
      list(
        rate_diff = formatters::with_label("", "Difference in Event Free Rate"),
        rate_diff_ci = formatters::with_label("", f_conf_level(control$conf_level)),
        ztest_pval = formatters::with_label("", "p-value (Z-test)")
      )
    )
  }
  data <- rbind(.ref_group, df)
  group <- factor(rep(c("ref", "x"), c(nrow(.ref_group), nrow(df))), levels = c("ref", "x"))
  res_per_group <- lapply(split(data, group), function(x) {
    s_surv_timepoint(df = x, .var = .var, time_point = time_point, control = control, ...)
  })

  res_x <- res_per_group[[2]]
  res_ref <- res_per_group[[1]]
  rate_diff <- res_x$event_free_rate - res_ref$event_free_rate
  se_diff <- sqrt(res_x$rate_se^2 + res_ref$rate_se^2)

  qs <- c(-1, 1) * stats::qnorm(1 - (1 - control$conf_level) / 2)
  rate_diff_ci <- rate_diff + qs * se_diff
  ztest_pval <- if (is.na(rate_diff)) {
    NA
  } else {
    2 * (1 - stats::pnorm(abs(rate_diff) / se_diff))
  }
  list(
    rate_diff = formatters::with_label(rate_diff, "Difference in Event Free Rate"),
    rate_diff_ci = formatters::with_label(rate_diff_ci, f_conf_level(control$conf_level)),
    ztest_pval = formatters::with_label(ztest_pval, "p-value (Z-test)")
  )
}

#' @describeIn survival_timepoint Formatted analysis function which is used as `afun` in `surv_timepoint()`
#'   when `method = "surv_diff"`.
#'
#' @return
#' * `a_surv_timepoint_diff()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_surv_timepoint_diff <- make_afun(
  s_surv_timepoint_diff,
  .formats = c(
    rate_diff = "xx.xx",
    rate_diff_ci = "(xx.xx, xx.xx)",
    ztest_pval = "x.xxxx | (<0.0001)"
  )
)

#' @describeIn survival_timepoint Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `surv_timepoint()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_surv_timepoint()` and/or `s_surv_timepoint_diff()` to the table layout depending on
#'   the value of `method`.
#'
#' @examples
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVAL = day2month(AVAL),
#'     is_event = CNSR == 0
#'   )
#'
#' # Survival at given time points.
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   surv_timepoint(
#'     vars = "AVAL",
#'     var_labels = "Months",
#'     is_event = "is_event",
#'     time_point = 7
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' # Difference in survival at given time points.
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   surv_timepoint(
#'     vars = "AVAL",
#'     var_labels = "Months",
#'     is_event = "is_event",
#'     time_point = 9,
#'     method = "surv_diff",
#'     .indent_mods = c("rate_diff" = 0L, "rate_diff_ci" = 2L, "ztest_pval" = 2L)
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' # Survival and difference in survival at given time points.
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   surv_timepoint(
#'     vars = "AVAL",
#'     var_labels = "Months",
#'     is_event = "is_event",
#'     time_point = 9,
#'     method = "both"
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
#' @order 2
surv_timepoint <- function(lyt,
                           vars,
                           time_point,
                           is_event,
                           control = control_surv_timepoint(),
                           method = c("surv", "surv_diff", "both"),
                           na_str = default_na_str(),
                           nested = TRUE,
                           ...,
                           table_names_suffix = "",
                           var_labels = "Time",
                           show_labels = "visible",
                           .stats = c(
                             "pt_at_risk", "event_free_rate", "rate_ci",
                             "rate_diff", "rate_diff_ci", "ztest_pval"
                           ),
                           .formats = NULL,
                           .labels = NULL,
                           .indent_mods = if (method == "both") {
                             c(rate_diff = 1L, rate_diff_ci = 2L, ztest_pval = 2L)
                           } else {
                             c(rate_diff_ci = 1L, ztest_pval = 1L)
                           }) {
  method <- match.arg(method)
  checkmate::assert_string(table_names_suffix)

  extra_args <- list(time_point = time_point, is_event = is_event, control = control, ...)

  f <- list(
    surv = c("pt_at_risk", "event_free_rate", "rate_se", "rate_ci"),
    surv_diff = c("rate_diff", "rate_diff_ci", "ztest_pval")
  )
  .stats <- h_split_param(.stats, .stats, f = f)
  .formats <- h_split_param(.formats, names(.formats), f = f)
  .labels <- h_split_param(.labels, names(.labels), f = f)
  .indent_mods <- h_split_param(.indent_mods, names(.indent_mods), f = f)

  afun_surv <- make_afun(
    a_surv_timepoint,
    .stats = .stats$surv,
    .formats = .formats$surv,
    .labels = .labels$surv,
    .indent_mods = .indent_mods$surv
  )

  afun_surv_diff <- make_afun(
    a_surv_timepoint_diff,
    .stats = .stats$surv_diff,
    .formats = .formats$surv_diff,
    .labels = .labels$surv_diff,
    .indent_mods = .indent_mods$surv_diff
  )

  time_point <- extra_args$time_point

  for (i in seq_along(time_point)) {
    extra_args[["time_point"]] <- time_point[i]

    if (method %in% c("surv", "both")) {
      lyt <- analyze(
        lyt,
        vars,
        var_labels = paste(time_point[i], var_labels),
        table_names = paste0("surv_", time_point[i], table_names_suffix),
        show_labels = show_labels,
        afun = afun_surv,
        na_str = na_str,
        nested = nested,
        extra_args = extra_args
      )
    }

    if (method %in% c("surv_diff", "both")) {
      lyt <- analyze(
        lyt,
        vars,
        var_labels = paste(time_point[i], var_labels),
        table_names = paste0("surv_diff_", time_point[i], table_names_suffix),
        show_labels = ifelse(method == "both", "hidden", show_labels),
        afun = afun_surv_diff,
        na_str = na_str,
        nested = nested,
        extra_args = extra_args
      )
    }
  }
  lyt
}

#' Patient Counts with Abnormal Range Values by Baseline Status
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Primary analysis variable `.var` indicates the abnormal range result (`character` or `factor`), and additional
#' analysis variables are `id` (`character` or `factor`) and `baseline` (`character` or `factor`). For each
#' direction specified in `abnormal` (e.g. high or low) we condition on baseline range result and count
#' patients in the numerator and denominator as follows:
#'   * `Not <Abnormal>`
#'     * `denom`: the number of patients without abnormality at baseline (excluding those with missing baseline)
#'     * `num`:  the number of patients in `denom` who also have at least one abnormality post-baseline
#'   * `<Abnormal>`
#'     * `denom`: the number of patients with abnormality at baseline
#'     * `num`: the number of patients in `denom` who also have at least one abnormality post-baseline
#'   * `Total`
#'     * `denom`: the number of patients with at least one valid measurement post-baseline
#'     * `num`: the number of patients in `denom` who also have at least one abnormality post-baseline
#'
#' @inheritParams argument_convention
#' @param abnormal (`character`)\cr identifying the abnormal range level(s) in `.var`.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("abnormal_by_baseline")`
#'   to see available statistics for this function.
#'
#' @note
#' * `df` should be filtered to include only post-baseline records.
#' * If the baseline variable or analysis variable contains `NA`, it is expected that `NA` has been
#'   conveyed to `na_level` appropriately beforehand with [df_explicit_na()] or [explicit_na()].
#'
#' @seealso Relevant description function [d_count_abnormal_by_baseline()].
#'
#' @name abnormal_by_baseline
#' @order 1
NULL

#' Description Function for [s_count_abnormal_by_baseline()]
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Description function that produces the labels for [s_count_abnormal_by_baseline()].
#'
#' @inheritParams abnormal_by_baseline
#'
#' @return Abnormal category labels for [s_count_abnormal_by_baseline()].
#'
#' @examples
#' d_count_abnormal_by_baseline("LOW")
#'
#' @export
d_count_abnormal_by_baseline <- function(abnormal) {
  not_abn_name <- paste("Not", tolower(abnormal))
  abn_name <- paste0(toupper(substr(abnormal, 1, 1)), tolower(substring(abnormal, 2)))
  total_name <- "Total"

  list(
    not_abnormal = not_abn_name,
    abnormal = abn_name,
    total = total_name
  )
}

#' @describeIn abnormal_by_baseline Statistics function for a single `abnormal` level.
#'
#' @param na_str (`string`)\cr the explicit `na_level` argument you used in the pre-processing steps (maybe with
#'   [df_explicit_na()]). The default is `"<Missing>"`.
#'
#' @return
#' * `s_count_abnormal_by_baseline()` returns statistic `fraction` which is a named list with 3 labeled elements:
#'   `not_abnormal`, `abnormal`, and `total`. Each element contains a vector with `num` and `denom` patient counts.
#'
#' @keywords internal
s_count_abnormal_by_baseline <- function(df,
                                         .var,
                                         abnormal,
                                         na_str = "<Missing>",
                                         variables = list(id = "USUBJID", baseline = "BNRIND")) {
  checkmate::assert_string(.var)
  checkmate::assert_string(abnormal)
  checkmate::assert_string(na_str)
  assert_df_with_variables(df, c(range = .var, variables))
  checkmate::assert_subset(names(variables), c("id", "baseline"))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[variables$baseline]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))

  # If input is passed as character, changed to factor
  df[[.var]] <- as_factor_keep_attributes(df[[.var]], na_level = na_str)
  df[[variables$baseline]] <- as_factor_keep_attributes(df[[variables$baseline]], na_level = na_str)

  assert_valid_factor(df[[.var]], any.missing = FALSE)
  assert_valid_factor(df[[variables$baseline]], any.missing = FALSE)

  # Keep only records with valid analysis value.
  df <- df[df[[.var]] != na_str, ]

  anl <- data.frame(
    id = df[[variables$id]],
    var = df[[.var]],
    baseline = df[[variables$baseline]],
    stringsAsFactors = FALSE
  )

  # Total:
  #  - Patients in denominator: have at least one valid measurement post-baseline.
  #  - Patients in numerator: have at least one abnormality.
  total_denom <- length(unique(anl$id))
  total_num <- length(unique(anl$id[anl$var == abnormal]))

  # Baseline NA records are counted only in total rows.
  anl <- anl[anl$baseline != na_str, ]

  # Abnormal:
  #   - Patients in denominator: have abnormality at baseline.
  #   - Patients in numerator: have abnormality at baseline AND
  #     have at least one abnormality post-baseline.
  abn_denom <- length(unique(anl$id[anl$baseline == abnormal]))
  abn_num <- length(unique(anl$id[anl$baseline == abnormal & anl$var == abnormal]))

  # Not abnormal:
  #   - Patients in denominator: do not have abnormality at baseline.
  #   - Patients in numerator: do not have abnormality at baseline AND
  #     have at least one abnormality post-baseline.
  not_abn_denom <- length(unique(anl$id[anl$baseline != abnormal]))
  not_abn_num <- length(unique(anl$id[anl$baseline != abnormal & anl$var == abnormal]))

  labels <- d_count_abnormal_by_baseline(abnormal)
  list(fraction = list(
    not_abnormal = formatters::with_label(c(num = not_abn_num, denom = not_abn_denom), labels$not_abnormal),
    abnormal = formatters::with_label(c(num = abn_num, denom = abn_denom), labels$abnormal),
    total = formatters::with_label(c(num = total_num, denom = total_denom), labels$total)
  ))
}

#' @describeIn abnormal_by_baseline Formatted analysis function which is used as `afun`
#'   in `count_abnormal_by_baseline()`.
#'
#' @return
#' * `a_count_abnormal_by_baseline()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_by_baseline <- make_afun(
  s_count_abnormal_by_baseline,
  .formats = c(fraction = format_fraction)
)

#' @describeIn abnormal_by_baseline Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_by_baseline()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal_by_baseline()` to the table layout.
#'
#' @examples
#' df <- data.frame(
#'   USUBJID = as.character(c(1:6)),
#'   ANRIND = factor(c(rep("LOW", 4), "NORMAL", "HIGH")),
#'   BNRIND = factor(c("LOW", "NORMAL", "HIGH", NA, "LOW", "NORMAL"))
#' )
#' df <- df_explicit_na(df)
#'
#' # Layout creating function.
#' basic_table() %>%
#'   count_abnormal_by_baseline(var = "ANRIND", abnormal = c(High = "HIGH")) %>%
#'   build_table(df)
#'
#' # Passing of statistics function and formatting arguments.
#' df2 <- data.frame(
#'   ID = as.character(c(1, 2, 3, 4)),
#'   RANGE = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
#'   BLRANGE = factor(c("LOW", "HIGH", "HIGH", "NORMAL"))
#' )
#'
#' basic_table() %>%
#'   count_abnormal_by_baseline(
#'     var = "RANGE",
#'     abnormal = c(Low = "LOW"),
#'     variables = list(id = "ID", baseline = "BLRANGE"),
#'     .formats = c(fraction = "xx / xx"),
#'     .indent_mods = c(fraction = 2L)
#'   ) %>%
#'   build_table(df2)
#'
#' @export
#' @order 2
count_abnormal_by_baseline <- function(lyt,
                                       var,
                                       abnormal,
                                       variables = list(id = "USUBJID", baseline = "BNRIND"),
                                       na_str = "<Missing>",
                                       nested = TRUE,
                                       ...,
                                       table_names = abnormal,
                                       .stats = NULL,
                                       .formats = NULL,
                                       .labels = NULL,
                                       .indent_mods = NULL) {
  checkmate::assert_character(abnormal, len = length(table_names), names = "named")
  checkmate::assert_string(var)

  extra_args <- list(abnormal = abnormal, variables = variables, na_str = na_str, ...)

  afun <- make_afun(
    a_count_abnormal_by_baseline,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "fraction"
  )
  for (i in seq_along(abnormal)) {
    extra_args[["abnormal"]] <- abnormal[i]

    lyt <- analyze(
      lyt = lyt,
      vars = var,
      var_labels = names(abnormal[i]),
      afun = afun,
      na_str = na_str,
      nested = nested,
      table_names = table_names[i],
      extra_args = extra_args,
      show_labels = "visible"
    )
  }
  lyt
}

#' `rtables` Access Helper Functions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' These are a couple of functions that help with accessing the data in `rtables` objects.
#' Currently these work for occurrence tables, which are defined as having a count as the first
#' element and a fraction as the second element in each cell.
#'
#' @seealso [prune_occurrences] for usage of these functions.
#'
#' @name rtables_access
NULL

#' @describeIn rtables_access Helper function to extract the first values from each content
#'   cell and from specified columns in a `TableRow`. Defaults to all columns.
#'
#' @param table_row (`TableRow`)\cr an analysis row in a occurrence table.
#' @param col_names (`character`)\cr the names of the columns to extract from.
#' @param col_indices (`integer`)\cr the indices of the columns to extract from. If `col_names` are provided,
#'   then these are inferred from the names of `table_row`. Note that this currently only works well with a single
#'   column split.
#'
#' @return
#' * `h_row_first_values()` returns a `vector` of numeric values.
#'
#' @examples
#' tbl <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("RACE") %>%
#'   analyze("AGE", function(x) {
#'     list(
#'       "mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.x (xx.x)"),
#'       "n" = length(x),
#'       "frac" = rcell(c(0.1, 0.1), format = "xx (xx)")
#'     )
#'   }) %>%
#'   build_table(tern_ex_adsl) %>%
#'   prune_table()
#' tree_row_elem <- collect_leaves(tbl[2, ])[[1]]
#' result <- max(h_row_first_values(tree_row_elem))
#' result
#'
#' @export
h_row_first_values <- function(table_row,
                               col_names = NULL,
                               col_indices = NULL) {
  col_indices <- check_names_indices(table_row, col_names, col_indices)
  checkmate::assert_integerish(col_indices)
  checkmate::assert_subset(col_indices, seq_len(ncol(table_row)))

  # Main values are extracted
  row_vals <- row_values(table_row)[col_indices]

  # Main return
  vapply(row_vals, function(rv) {
    if (is.null(rv)) {
      NA_real_
    } else {
      rv[1L]
    }
  }, FUN.VALUE = numeric(1))
}

#' @describeIn rtables_access Helper function that extracts row values and checks if they are
#'   convertible to integers (`integerish` values).
#'
#' @return
#' * `h_row_counts()` returns a `vector` of numeric values.
#'
#' @examples
#' # Row counts (integer values)
#' # h_row_counts(tree_row_elem) # Fails because there are no integers
#' # Using values with integers
#' tree_row_elem <- collect_leaves(tbl[3, ])[[1]]
#' result <- h_row_counts(tree_row_elem)
#' # result
#'
#' @export
h_row_counts <- function(table_row,
                         col_names = NULL,
                         col_indices = NULL) {
  counts <- h_row_first_values(table_row, col_names, col_indices)
  checkmate::assert_integerish(counts)
  counts
}

#' @describeIn rtables_access helper function to extract fractions from specified columns in a `TableRow`.
#'   More specifically it extracts the second values from each content cell and checks it is a fraction.
#'
#' @return
#' * `h_row_fractions()` returns a `vector` of proportions.
#'
#' @examples
#' # Row fractions
#' tree_row_elem <- collect_leaves(tbl[4, ])[[1]]
#' h_row_fractions(tree_row_elem)
#'
#' @export
h_row_fractions <- function(table_row,
                            col_names = NULL,
                            col_indices = NULL) {
  col_indices <- check_names_indices(table_row, col_names, col_indices)
  row_vals <- row_values(table_row)[col_indices]
  fractions <- sapply(row_vals, "[", 2L)
  checkmate::assert_numeric(fractions, lower = 0, upper = 1)
  fractions
}

#' @describeIn rtables_access Helper function to extract column counts from specified columns in a table.
#'
#' @param table (`VTableNodeInfo`)\cr an occurrence table or row.
#'
#' @return
#' * `h_col_counts()` returns a `vector` of column counts.
#'
#' @export
h_col_counts <- function(table,
                         col_names = NULL,
                         col_indices = NULL) {
  col_indices <- check_names_indices(table, col_names, col_indices)
  counts <- col_counts(table)[col_indices]
  stats::setNames(counts, col_names)
}

#' @describeIn rtables_access Helper function to get first row of content table of current table.
#'
#' @return
#' * `h_content_first_row()` returns a row from an `rtables` table.
#'
#' @export
h_content_first_row <- function(table) {
  ct <- content_table(table)
  tree_children(ct)[[1]]
}

#' @describeIn rtables_access Helper function which says whether current table is a leaf in the tree.
#'
#' @return
#' * `is_leaf_table()` returns a `logical` value indicating whether current table is a leaf.
#'
#' @keywords internal
is_leaf_table <- function(table) {
  children <- tree_children(table)
  child_classes <- unique(sapply(children, class))
  identical(child_classes, "ElementaryTable")
}

#' @describeIn rtables_access Internal helper function that tests standard inputs for column indices.
#'
#' @return
#' * `check_names_indices` returns column indices.
#'
#' @keywords internal
check_names_indices <- function(table_row,
                                col_names = NULL,
                                col_indices = NULL) {
  if (!is.null(col_names)) {
    if (!is.null(col_indices)) {
      stop(
        "Inserted both col_names and col_indices when selecting row values. ",
        "Please choose one."
      )
    }
    col_indices <- h_col_indices(table_row, col_names)
  }
  if (is.null(col_indices)) {
    ll <- ifelse(is.null(ncol(table_row)), length(table_row), ncol(table_row))
    col_indices <- seq_len(ll)
  }

  return(col_indices)
}

#' Confidence Interval for Mean
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convenient function for calculating the mean confidence interval. It calculates the arithmetic as well as the
#' geometric mean. It can be used as a `ggplot` helper function for plotting.
#'
#' @inheritParams argument_convention
#' @param n_min (`number`)\cr a minimum number of non-missing `x` to estimate the confidence interval for mean.
#' @param gg_helper (`logical`)\cr `TRUE` when output should be aligned for the use with `ggplot`.
#' @param geom_mean (`logical`)\cr `TRUE` when the geometric mean should be calculated.
#'
#' @return A named `vector` of values `mean_ci_lwr` and `mean_ci_upr`.
#'
#' @examples
#' stat_mean_ci(sample(10), gg_helper = FALSE)
#'
#' p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +
#'   ggplot2::geom_point()
#'
#' p + ggplot2::stat_summary(
#'   fun.data = stat_mean_ci,
#'   geom = "errorbar"
#' )
#'
#' p + ggplot2::stat_summary(
#'   fun.data = stat_mean_ci,
#'   fun.args = list(conf_level = 0.5),
#'   geom = "errorbar"
#' )
#'
#' p + ggplot2::stat_summary(
#'   fun.data = stat_mean_ci,
#'   fun.args = list(conf_level = 0.5, geom_mean = TRUE),
#'   geom = "errorbar"
#' )
#'
#' @export
stat_mean_ci <- function(x,
                         conf_level = 0.95,
                         na.rm = TRUE, # nolint
                         n_min = 2,
                         gg_helper = TRUE,
                         geom_mean = FALSE) {
  if (na.rm) {
    x <- stats::na.omit(x)
  }
  n <- length(x)

  if (!geom_mean) {
    m <- mean(x)
  } else {
    negative_values_exist <- any(is.na(x[!is.na(x)]) <- x[!is.na(x)] <= 0)
    if (negative_values_exist) {
      m <- NA_real_
    } else {
      x <- log(x)
      m <- mean(x)
    }
  }

  if (n < n_min || is.na(m)) {
    ci <- c(mean_ci_lwr = NA_real_, mean_ci_upr = NA_real_)
  } else {
    hci <- stats::qt((1 + conf_level) / 2, df = n - 1) * stats::sd(x) / sqrt(n)
    ci <- c(mean_ci_lwr = m - hci, mean_ci_upr = m + hci)
    if (geom_mean) {
      ci <- exp(ci)
    }
  }

  if (gg_helper) {
    m <- ifelse(is.na(m), NA_real_, m)
    ci <- data.frame(y = ifelse(geom_mean, exp(m), m), ymin = ci[[1]], ymax = ci[[2]])
  }

  return(ci)
}

#' Confidence Interval for Median
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convenient function for calculating the median confidence interval. It can be used as a `ggplot` helper
#' function for plotting.
#'
#' @inheritParams argument_convention
#' @param gg_helper (`logical`)\cr `TRUE` when output should be aligned for the use with `ggplot`.
#'
#' @details The function was adapted from `DescTools/versions/0.99.35/source`
#'
#' @return A named `vector` of values `median_ci_lwr` and `median_ci_upr`.
#'
#' @examples
#' stat_median_ci(sample(10), gg_helper = FALSE)
#'
#' p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +
#'   ggplot2::geom_point()
#' p + ggplot2::stat_summary(
#'   fun.data = stat_median_ci,
#'   geom = "errorbar"
#' )
#'
#' @export
stat_median_ci <- function(x,
                           conf_level = 0.95,
                           na.rm = TRUE, # nolint
                           gg_helper = TRUE) {
  x <- unname(x)
  if (na.rm) {
    x <- x[!is.na(x)]
  }
  n <- length(x)
  med <- stats::median(x)

  k <- stats::qbinom(p = (1 - conf_level) / 2, size = n, prob = 0.5, lower.tail = TRUE)

  # k == 0 - for small samples (e.g. n <= 5) ci can be outside the observed range
  if (k == 0 || is.na(med)) {
    ci <- c(median_ci_lwr = NA_real_, median_ci_upr = NA_real_)
    empir_conf_level <- NA_real_
  } else {
    x_sort <- sort(x)
    ci <- c(median_ci_lwr = x_sort[k], median_ci_upr = x_sort[n - k + 1])
    empir_conf_level <- 1 - 2 * stats::pbinom(k - 1, size = n, prob = 0.5)
  }

  if (gg_helper) {
    ci <- data.frame(y = med, ymin = ci[[1]], ymax = ci[[2]])
  }

  attr(ci, "conf_level") <- empir_conf_level

  return(ci)
}

#' p-Value of the Mean
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convenient function for calculating the two-sided p-value of the mean.
#'
#' @inheritParams argument_convention
#' @param n_min (`numeric`)\cr a minimum number of non-missing `x` to estimate the p-value of the mean.
#' @param test_mean (`numeric`)\cr mean value to test under the null hypothesis.
#'
#' @return A p-value.
#'
#' @examples
#' stat_mean_pval(sample(10))
#'
#' stat_mean_pval(rnorm(10), test_mean = 0.5)
#'
#' @export
stat_mean_pval <- function(x,
                           na.rm = TRUE, # nolint
                           n_min = 2,
                           test_mean = 0) {
  if (na.rm) {
    x <- stats::na.omit(x)
  }
  n <- length(x)

  x_mean <- mean(x)
  x_sd <- stats::sd(x)

  if (n < n_min) {
    pv <- c(p_value = NA_real_)
  } else {
    x_se <- stats::sd(x) / sqrt(n)
    ttest <- (x_mean - test_mean) / x_se
    pv <- c(p_value = 2 * stats::pt(-abs(ttest), df = n - 1))
  }

  return(pv)
}

#' Proportion Difference and Confidence Interval
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Function for calculating the proportion (or risk) difference and confidence interval between arm
#' X (reference group) and arm Y. Risk difference is calculated by subtracting cumulative incidence
#' in arm Y from cumulative incidence in arm X.
#'
#' @inheritParams argument_convention
#' @param x (`list` of `integer`)\cr list of number of occurrences in arm X (reference group).
#' @param y (`list` of `integer`)\cr list of number of occurrences in arm Y. Must be of equal length to `x`.
#' @param N_x (`numeric`)\cr total number of records in arm X.
#' @param N_y (`numeric`)\cr total number of records in arm Y.
#' @param list_names (`character`)\cr names of each variable/level corresponding to pair of proportions in
#'   `x` and `y`. Must be of equal length to `x` and `y`.
#' @param pct (`flag`)\cr whether output should be returned as percentages. Defaults to `TRUE`.
#'
#' @return List of proportion differences and CIs corresponding to each pair of number of occurrences in `x` and
#'   `y`. Each list element consists of 3 statistics: proportion difference, CI lower bound, and CI upper bound.
#'
#' @seealso Split function [add_riskdiff()] which, when used as `split_fun` within [rtables::split_cols_by()]
#'   with `riskdiff` argument is set to `TRUE` in subsequent analyze functions, adds a column containing
#'   proportion (risk) difference to an `rtables` layout.
#'
#' @examples
#' stat_propdiff_ci(
#'   x = list(0.375), y = list(0.01), N_x = 5, N_y = 5, list_names = "x", conf_level = 0.9
#' )
#'
#' stat_propdiff_ci(
#'   x = list(0.5, 0.75, 1), y = list(0.25, 0.05, 0.5), N_x = 10, N_y = 20, pct = FALSE
#' )
#'
#' @export
stat_propdiff_ci <- function(x,
                             y,
                             N_x, # nolint
                             N_y, # nolint
                             list_names = NULL,
                             conf_level = 0.95,
                             pct = TRUE) {
  checkmate::assert_list(x, types = "numeric")
  checkmate::assert_list(y, types = "numeric", len = length(x))
  checkmate::assert_character(list_names, len = length(x), null.ok = TRUE)
  rd_list <- lapply(seq_along(x), function(i) {
    p_x <- x[[i]] / N_x
    p_y <- y[[i]] / N_y
    rd_ci <- p_x - p_y + c(-1, 1) * stats::qnorm((1 + conf_level) / 2) *
      sqrt(p_x * (1 - p_x) / N_x + p_y * (1 - p_y) / N_y)
    c(p_x - p_y, rd_ci) * ifelse(pct, 100, 1)
  })
  names(rd_list) <- list_names
  rd_list
}

#' Convert `rtable` object to `ggplot` object
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Given a [rtables::rtable()] object, performs basic conversion to a [ggplot2::ggplot()] object built using
#' functions from the `ggplot2` package. Any table titles and/or footnotes are ignored.
#'
#' @param tbl (`rtable`)\cr a `rtable` object.
#' @param fontsize (`numeric`)\cr font size.
#' @param colwidths (`vector` of `numeric`)\cr a vector of column widths. Each element's position in
#'   `colwidths` corresponds to the column of `tbl` in the same position. If `NULL`, column widths
#'   are calculated according to maximum number of characters per column.
#' @param lbl_col_padding (`numeric`)\cr additional padding to use when calculating spacing between
#'   the first (label) column and the second column of `tbl`. If `colwidths` is specified,
#'   the width of the first column becomes `colwidths[1] + lbl_col_padding`. Defaults to 0.
#'
#' @return a `ggplot` object.
#'
#' @examples
#' dta <- data.frame(
#'   ARM     = rep(LETTERS[1:3], rep(6, 3)),
#'   AVISIT  = rep(paste0("V", 1:3), 6),
#'   AVAL    = c(9:1, rep(NA, 9))
#' )
#'
#' lyt <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(vars = "AVAL")
#'
#' tbl <- build_table(lyt, df = dta)
#'
#' rtable2gg(tbl)
#'
#' rtable2gg(tbl, fontsize = 15, colwidths = c(2, 1, 1, 1))
#'
#' @export
rtable2gg <- function(tbl, fontsize = 12, colwidths = NULL, lbl_col_padding = 0) {
  mat <- rtables::matrix_form(tbl)
  mat_strings <- formatters::mf_strings(mat)
  mat_aligns <- formatters::mf_aligns(mat)
  mat_indent <- formatters::mf_rinfo(mat)$indent
  mat_display <- formatters::mf_display(mat)
  nlines_hdr <- formatters::mf_nlheader(mat)
  shared_hdr_rows <- which(apply(mat_display, 1, function(x) (any(!x))))

  tbl_df <- data.frame(mat_strings)
  body_rows <- seq(nlines_hdr + 1, nrow(tbl_df))
  mat_aligns <- apply(mat_aligns, 1:2, function(x) if (x == "left") 0 else if (x == "right") 1 else 0.5)

  # Apply indentation in first column
  tbl_df[body_rows, 1] <- sapply(body_rows, function(i) {
    ind_i <- mat_indent[i - nlines_hdr] * 4
    if (ind_i > 0) paste0(paste(rep(" ", ind_i), collapse = ""), tbl_df[i, 1]) else tbl_df[i, 1]
  })

  # Get column widths
  if (is.null(colwidths)) {
    colwidths <- apply(tbl_df, 2, function(x) max(nchar(x))) + 1
  }
  tot_width <- sum(colwidths) + lbl_col_padding

  if (length(shared_hdr_rows) > 0) {
    tbl_df <- tbl_df[-shared_hdr_rows, ]
    mat_aligns <- mat_aligns[-shared_hdr_rows, ]
  }

  res <- ggplot(data = tbl_df) +
    theme_void() +
    scale_x_continuous(limits = c(0, tot_width)) +
    scale_y_continuous(limits = c(0, nrow(mat_strings))) +
    annotate(
      "segment",
      x = 0, xend = tot_width,
      y = nrow(mat_strings) - nlines_hdr + 0.5, yend = nrow(mat_strings) - nlines_hdr + 0.5
    )

  # If header content spans multiple columns, center over these columns
  if (length(shared_hdr_rows) > 0) {
    mat_strings[shared_hdr_rows, ] <- trimws(mat_strings[shared_hdr_rows, ])
    for (hr in shared_hdr_rows) {
      hdr_lbls <- mat_strings[1:hr, mat_display[hr, -1]]
      hdr_lbls <- matrix(hdr_lbls[nzchar(hdr_lbls)], nrow = hr)
      for (idx_hl in seq_len(ncol(hdr_lbls))) {
        cur_lbl <- tail(hdr_lbls[, idx_hl], 1)
        which_cols <- if (hr == 1) {
          which(mat_strings[hr, ] == hdr_lbls[idx_hl])
        } else { # for >2 col splits, only print labels for each unique combo of nested columns
          which(
            apply(mat_strings[1:hr, ], 2, function(x) all(x == hdr_lbls[1:hr, idx_hl]))
          )
        }
        line_pos <- c(
          sum(colwidths[1:(which_cols[1] - 1)]) + 1 + lbl_col_padding,
          sum(colwidths[1:max(which_cols)]) - 1 + lbl_col_padding
        )

        res <- res +
          annotate(
            "text",
            x = mean(line_pos),
            y = nrow(mat_strings) + 1 - hr,
            label = cur_lbl,
            size = fontsize / .pt
          ) +
          annotate(
            "segment",
            x = line_pos[1],
            xend = line_pos[2],
            y = nrow(mat_strings) - hr + 0.5,
            yend = nrow(mat_strings) - hr + 0.5
          )
      }
    }
  }

  # Add table columns
  for (i in seq_len(ncol(tbl_df))) {
    res <- res + annotate(
      "text",
      x = if (i == 1) 0 else sum(colwidths[1:i]) - 0.5 * colwidths[i] + lbl_col_padding,
      y = rev(seq_len(nrow(tbl_df))),
      label = tbl_df[, i],
      hjust = mat_aligns[, i],
      size = fontsize / .pt
    )
  }

  res
}

#' Pairwise `CoxPH` model
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Summarize p-value, HR and CIs from stratified or unstratified `CoxPH` model.
#'
#' @inheritParams argument_convention
#' @inheritParams s_surv_time
#' @param strata (`character` or `NULL`)\cr variable names indicating stratification factors.
#' @param strat `r lifecycle::badge("deprecated")` Please use the `strata` argument instead.
#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function
#'   [control_coxph()]. Some possible parameter options are:
#'   * `pval_method` (`string`)\cr p-value method for testing hazard ratio = 1. Default method is `"log-rank"` which
#'     comes from [survival::survdiff()], can also be set to `"wald"` or `"likelihood"` (from [survival::coxph()]).
#'   * `ties` (`string`)\cr specifying the method for tie handling. Default is `"efron"`,
#'     can also be set to `"breslow"` or `"exact"`. See more in [survival::coxph()]
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for HR.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("coxph_pairwise")`
#'   to see available statistics for this function.
#'
#' @name survival_coxph_pairwise
#' @order 1
NULL

#' @describeIn survival_coxph_pairwise Statistics function which analyzes HR, CIs of HR and p-value of a `coxph` model.
#'
#' @return
#' * `s_coxph_pairwise()` returns the statistics:
#'   * `pvalue`: p-value to test HR = 1.
#'   * `hr`: Hazard ratio.
#'   * `hr_ci`: Confidence interval for hazard ratio.
#'   * `n_tot`: Total number of observations.
#'   * `n_tot_events`: Total number of events.
#'
#' @keywords internal
s_coxph_pairwise <- function(df,
                             .ref_group,
                             .in_ref_col,
                             .var,
                             is_event,
                             strata = NULL,
                             strat = lifecycle::deprecated(),
                             control = control_coxph()) {
  if (lifecycle::is_present(strat)) {
    lifecycle::deprecate_warn("0.9.3", "s_coxph_pairwise(strat)", "s_coxph_pairwise(strata)")
    strata <- strat
  }

  checkmate::assert_string(.var)
  checkmate::assert_numeric(df[[.var]])
  checkmate::assert_logical(df[[is_event]])
  assert_df_with_variables(df, list(tte = .var, is_event = is_event))
  pval_method <- control$pval_method
  ties <- control$ties
  conf_level <- control$conf_level

  if (.in_ref_col) {
    return(
      list(
        pvalue = formatters::with_label("", paste0("p-value (", pval_method, ")")),
        hr = formatters::with_label("", "Hazard Ratio"),
        hr_ci = formatters::with_label("", f_conf_level(conf_level)),
        n_tot = formatters::with_label("", "Total n"),
        n_tot_events = formatters::with_label("", "Total events")
      )
    )
  }
  data <- rbind(.ref_group, df)
  group <- factor(rep(c("ref", "x"), c(nrow(.ref_group), nrow(df))), levels = c("ref", "x"))

  df_cox <- data.frame(
    tte = data[[.var]],
    is_event = data[[is_event]],
    arm = group
  )
  if (is.null(strata)) {
    formula_cox <- survival::Surv(tte, is_event) ~ arm
  } else {
    formula_cox <- stats::as.formula(
      paste0(
        "survival::Surv(tte, is_event) ~ arm + strata(",
        paste(strata, collapse = ","),
        ")"
      )
    )
    df_cox <- cbind(df_cox, data[strata])
  }
  cox_fit <- survival::coxph(
    formula = formula_cox,
    data = df_cox,
    ties = ties
  )
  sum_cox <- summary(cox_fit, conf.int = conf_level, extend = TRUE)
  orginal_survdiff <- survival::survdiff(
    formula_cox,
    data = df_cox
  )
  log_rank_pvalue <- 1 - pchisq(orginal_survdiff$chisq, length(orginal_survdiff$n) - 1)

  pval <- switch(pval_method,
    "wald" = sum_cox$waldtest["pvalue"],
    "log-rank" = log_rank_pvalue, # pvalue from original log-rank test survival::survdiff()
    "likelihood" = sum_cox$logtest["pvalue"]
  )
  list(
    pvalue = formatters::with_label(unname(pval), paste0("p-value (", pval_method, ")")),
    hr = formatters::with_label(sum_cox$conf.int[1, 1], "Hazard Ratio"),
    hr_ci = formatters::with_label(unname(sum_cox$conf.int[1, 3:4]), f_conf_level(conf_level)),
    n_tot = formatters::with_label(sum_cox$n, "Total n"),
    n_tot_events = formatters::with_label(sum_cox$nevent, "Total events")
  )
}

#' @describeIn survival_coxph_pairwise Formatted analysis function which is used as `afun` in `coxph_pairwise()`.
#'
#' @return
#' * `a_coxph_pairwise()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_coxph_pairwise <- make_afun(
  s_coxph_pairwise,
  .indent_mods = c(pvalue = 0L, hr = 0L, hr_ci = 1L, n_tot = 0L, n_tot_events = 0L),
  .formats = c(
    pvalue = "x.xxxx | (<0.0001)",
    hr = "xx.xx",
    hr_ci = "(xx.xx, xx.xx)",
    n_tot = "xx.xx",
    n_tot_events = "xx.xx"
  )
)

#' @describeIn survival_coxph_pairwise Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `coxph_pairwise()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_coxph_pairwise()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#'
#' df <- adtte_f %>% filter(ARMCD == "ARM A")
#' df_ref_group <- adtte_f %>% filter(ARMCD == "ARM B")
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   coxph_pairwise(
#'     vars = "AVAL",
#'     is_event = "is_event",
#'     var_labels = "Unstratified Analysis"
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   coxph_pairwise(
#'     vars = "AVAL",
#'     is_event = "is_event",
#'     var_labels = "Stratified Analysis",
#'     strata = "SEX",
#'     control = control_coxph(pval_method = "wald")
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
#' @order 2
coxph_pairwise <- function(lyt,
                           vars,
                           na_str = default_na_str(),
                           nested = TRUE,
                           ...,
                           var_labels = "CoxPH",
                           show_labels = "visible",
                           table_names = vars,
                           .stats = c("pvalue", "hr", "hr_ci"),
                           .formats = NULL,
                           .labels = NULL,
                           .indent_mods = NULL) {
  extra_args <- list(...)

  afun <- make_afun(
    a_coxph_pairwise,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  analyze(
    lyt,
    vars,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Estimation of Proportions per Level of Factor
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Estimate the proportion along with confidence interval of a proportion
#' regarding the level of a factor.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_multinomial_response")`
#'   to see available statistics for this function.
#'
#' @seealso Relevant description function [d_onco_rsp_label()].
#'
#' @name estimate_multinomial_rsp
#' @order 1
NULL

#' Description of Standard Oncology Response
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Describe the oncology response in a standard way.
#'
#' @param x (`character`)\cr the standard oncology code to be described.
#'
#' @return Response labels.
#'
#' @seealso [estimate_multinomial_rsp()]
#'
#' @examples
#' d_onco_rsp_label(
#'   c("CR", "PR", "SD", "NON CR/PD", "PD", "NE", "Missing", "<Missing>", "NE/Missing")
#' )
#'
#' # Adding some values not considered in d_onco_rsp_label
#'
#' d_onco_rsp_label(
#'   c("CR", "PR", "hello", "hi")
#' )
#'
#' @export
d_onco_rsp_label <- function(x) {
  x <- as.character(x)
  desc <- c(
    CR           = "Complete Response (CR)",
    PR           = "Partial Response (PR)",
    MR           = "Minimal/Minor Response (MR)",
    MRD          = "Minimal Residual Disease (MRD)",
    SD           = "Stable Disease (SD)",
    PD           = "Progressive Disease (PD)",
    `NON CR/PD`  = "Non-CR or Non-PD (NON CR/PD)",
    NE           = "Not Evaluable (NE)",
    `NE/Missing` = "Missing or unevaluable",
    Missing      = "Missing",
    `NA`         = "Not Applicable (NA)",
    ND           = "Not Done (ND)"
  )

  values_label <- vapply(
    X = x,
    FUN.VALUE = character(1),
    function(val) {
      if (val %in% names(desc)) desc[val] else val
    }
  )

  return(factor(values_label, levels = c(intersect(desc, values_label), setdiff(values_label, desc))))
}

#' @describeIn estimate_multinomial_rsp Statistics function which feeds the length of `x` as number
#'   of successes, and `.N_col` as total number of successes and failures into [s_proportion()].
#'
#' @return
#' * `s_length_proportion()` returns statistics from [s_proportion()].
#'
#' @examples
#' s_length_proportion(rep("CR", 10), .N_col = 100)
#' s_length_proportion(factor(character(0)), .N_col = 100)
#'
#' @export
s_length_proportion <- function(x,
                                .N_col, # nolint
                                ...) {
  checkmate::assert_multi_class(x, classes = c("factor", "character"))
  checkmate::assert_vector(x, min.len = 0, max.len = .N_col)
  checkmate::assert_vector(unique(x), min.len = 0, max.len = 1)

  n_true <- length(x)
  n_false <- .N_col - n_true
  x_logical <- rep(c(TRUE, FALSE), c(n_true, n_false))
  s_proportion(df = x_logical, ...)
}

#' @describeIn estimate_multinomial_rsp Formatted analysis function which is used as `afun`
#'   in `estimate_multinomial_response()`.
#'
#' @return
#' * `a_length_proportion()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_length_proportion(rep("CR", 10), .N_col = 100)
#' a_length_proportion(factor(character(0)), .N_col = 100)
#'
#' @export
a_length_proportion <- make_afun(
  s_length_proportion,
  .formats = c(
    n_prop = "xx (xx.x%)",
    prop_ci = "(xx.xx, xx.xx)"
  )
)

#' @describeIn estimate_multinomial_rsp Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()] and
#'   [rtables::summarize_row_groups()].
#'
#' @return
#' * `estimate_multinomial_response()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_length_proportion()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' # Use of the layout creating function.
#' dta_test <- data.frame(
#'   USUBJID = paste0("S", 1:12),
#'   ARM     = factor(rep(LETTERS[1:3], each = 4)),
#'   AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0))
#' ) %>% mutate(
#'   AVALC = factor(AVAL,
#'     levels = c(0, 1),
#'     labels = c("Complete Response (CR)", "Partial Response (PR)")
#'   )
#' )
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   estimate_multinomial_response(var = "AVALC")
#'
#' tbl <- build_table(lyt, dta_test)
#'
#' tbl
#'
#' @export
#' @order 2
estimate_multinomial_response <- function(lyt,
                                          var,
                                          na_str = default_na_str(),
                                          nested = TRUE,
                                          ...,
                                          show_labels = "hidden",
                                          table_names = var,
                                          .stats = "prop_ci",
                                          .formats = NULL,
                                          .labels = NULL,
                                          .indent_mods = NULL) {
  extra_args <- list(...)

  afun <- make_afun(
    a_length_proportion,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  lyt <- split_rows_by(lyt, var = var)
  lyt <- summarize_row_groups(lyt, na_str = na_str)

  analyze(
    lyt,
    vars = var,
    afun = afun,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Summary for analysis of covariance (`ANCOVA`).
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Summarize results of `ANCOVA`. This can be used to analyze multiple endpoints and/or
#' multiple timepoints within the same response variable `.var`.
#'
#' @inheritParams h_ancova
#' @inheritParams argument_convention
#' @param interaction_y (`character`)\cr a selected item inside of the interaction_item column which will be used
#'   to select the specific `ANCOVA` results. if the interaction is not needed, the default option is `FALSE`.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("summarize_ancova")`
#'   to see available statistics for this function.
#'
#' @name summarize_ancova
#' @order 1
NULL

#' Helper Function to Return Results of a Linear Model
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called in `.var` and `variables`.
#' @param variables (named `list` of `strings`)\cr list of additional analysis variables, with expected elements:
#'   * `arm` (`string`)\cr group variable, for which the covariate adjusted means of multiple groups will be
#'     summarized. Specifically, the first level of `arm` variable is taken as the reference group.
#'   * `covariates` (`character`)\cr a vector that can contain single variable names (such as `"X1"`), and/or
#'     interaction terms indicated by `"X1 * X2"`.
#' @param interaction_item (`character`)\cr name of the variable that should have interactions
#'   with arm. if the interaction is not needed, the default option is `NULL`.
#'
#' @return The summary of a linear model.
#'
#' @examples
#' h_ancova(
#'   .var = "Sepal.Length",
#'   .df_row = iris,
#'   variables = list(arm = "Species", covariates = c("Petal.Length * Petal.Width", "Sepal.Width"))
#' )
#'
#' @export
h_ancova <- function(.var,
                     .df_row,
                     variables,
                     interaction_item = NULL) {
  checkmate::assert_string(.var)
  checkmate::assert_list(variables)
  checkmate::assert_subset(names(variables), c("arm", "covariates"))
  assert_df_with_variables(.df_row, list(rsp = .var))

  arm <- variables$arm
  covariates <- variables$covariates
  if (!is.null(covariates) && length(covariates) > 0) {
    # Get all covariate variable names in the model.
    var_list <- get_covariates(covariates)
    assert_df_with_variables(.df_row, var_list)
  }

  covariates_part <- paste(covariates, collapse = " + ")
  if (covariates_part != "") {
    formula <- stats::as.formula(paste0(.var, " ~ ", covariates_part, " + ", arm))
  } else {
    formula <- stats::as.formula(paste0(.var, " ~ ", arm))
  }

  if (is.null(interaction_item)) {
    specs <- arm
  } else {
    specs <- c(arm, interaction_item)
  }

  lm_fit <- stats::lm(
    formula = formula,
    data = .df_row
  )
  emmeans_fit <- emmeans::emmeans(
    lm_fit,
    # Specify here the group variable over which EMM are desired.
    specs = specs,
    # Pass the data again so that the factor levels of the arm variable can be inferred.
    data = .df_row
  )

  emmeans_fit
}

#' @describeIn summarize_ancova Statistics function that produces a named list of results
#'   of the investigated linear model.
#'
#' @return
#' * `s_ancova()` returns a named list of 5 statistics:
#'   * `n`: Count of complete sample size for the group.
#'   * `lsmean`: Estimated marginal means in the group.
#'   * `lsmean_diff`: Difference in estimated marginal means in comparison to the reference group.
#'     If working with the reference group, this will be empty.
#'   * `lsmean_diff_ci`: Confidence level for difference in estimated marginal means in comparison
#'     to the reference group.
#'   * `pval`: p-value (not adjusted for multiple comparisons).
#'
#' @keywords internal
s_ancova <- function(df,
                     .var,
                     .df_row,
                     variables,
                     .ref_group,
                     .in_ref_col,
                     conf_level,
                     interaction_y = FALSE,
                     interaction_item = NULL) {
  emmeans_fit <- h_ancova(.var = .var, variables = variables, .df_row = .df_row, interaction_item = interaction_item)

  sum_fit <- summary(
    emmeans_fit,
    level = conf_level
  )

  arm <- variables$arm

  sum_level <- as.character(unique(df[[arm]]))

  # Ensure that there is only one element in sum_level.
  checkmate::assert_scalar(sum_level)

  sum_fit_level <- sum_fit[sum_fit[[arm]] == sum_level, ]

  # Get the index of the ref arm
  if (interaction_y != FALSE) {
    y <- unlist(df[(df[[interaction_item]] == interaction_y), .var])
    # convert characters selected in interaction_y into the numeric order
    interaction_y <- which(sum_fit_level[[interaction_item]] == interaction_y)
    sum_fit_level <- sum_fit_level[interaction_y, ]
    # if interaction is called, reset the index
    ref_key <- seq(sum_fit[[arm]][unique(.ref_group[[arm]])])
    ref_key <- tail(ref_key, n = 1)
    ref_key <- (interaction_y - 1) * length(unique(.df_row[[arm]])) + ref_key
  } else {
    y <- df[[.var]]
    # Get the index of the ref arm when interaction is not called
    ref_key <- seq(sum_fit[[arm]][unique(.ref_group[[arm]])])
    ref_key <- tail(ref_key, n = 1)
  }

  if (.in_ref_col) {
    list(
      n = length(y[!is.na(y)]),
      lsmean = formatters::with_label(sum_fit_level$emmean, "Adjusted Mean"),
      lsmean_diff = formatters::with_label(character(), "Difference in Adjusted Means"),
      lsmean_diff_ci = formatters::with_label(character(), f_conf_level(conf_level)),
      pval = formatters::with_label(character(), "p-value")
    )
  } else {
    # Estimate the differences between the marginal means.
    emmeans_contrasts <- emmeans::contrast(
      emmeans_fit,
      # Compare all arms versus the control arm.
      method = "trt.vs.ctrl",
      # Take the arm factor from .ref_group as the control arm.
      ref = ref_key,
      level = conf_level
    )
    sum_contrasts <- summary(
      emmeans_contrasts,
      # Derive confidence intervals, t-tests and p-values.
      infer = TRUE,
      # Do not adjust the p-values for multiplicity.
      adjust = "none"
    )

    contrast_lvls <- gsub(paste0(" - ", .ref_group[[arm]][1], ".*"), "", sum_contrasts$contrast)
    if (!is.null(interaction_item)) {
      sum_contrasts_level <- sum_contrasts[grepl(sum_level, contrast_lvls, fixed = TRUE), ]
    } else {
      sum_contrasts_level <- sum_contrasts[sum_level == contrast_lvls, ]
    }
    if (interaction_y != FALSE) {
      sum_contrasts_level <- sum_contrasts_level[interaction_y, ]
    }

    list(
      n = length(y[!is.na(y)]),
      lsmean = formatters::with_label(sum_fit_level$emmean, "Adjusted Mean"),
      lsmean_diff = formatters::with_label(sum_contrasts_level$estimate, "Difference in Adjusted Means"),
      lsmean_diff_ci = formatters::with_label(
        c(sum_contrasts_level$lower.CL, sum_contrasts_level$upper.CL),
        f_conf_level(conf_level)
      ),
      pval = formatters::with_label(sum_contrasts_level$p.value, "p-value")
    )
  }
}

#' @describeIn summarize_ancova Formatted analysis function which is used as `afun` in `summarize_ancova()`.
#'
#' @return
#' * `a_ancova()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_ancova <- make_afun(
  s_ancova,
  .indent_mods = c("n" = 0L, "lsmean" = 0L, "lsmean_diff" = 0L, "lsmean_diff_ci" = 1L, "pval" = 1L),
  .formats = c(
    "n" = "xx",
    "lsmean" = "xx.xx",
    "lsmean_diff" = "xx.xx",
    "lsmean_diff_ci" = "(xx.xx, xx.xx)",
    "pval" = "x.xxxx | (<0.0001)"
  ),
  .null_ref_cells = FALSE
)

#' @describeIn summarize_ancova Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `summarize_ancova()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_ancova()` to the table layout.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by("Species", ref_group = "setosa") %>%
#'   add_colcounts() %>%
#'   summarize_ancova(
#'     vars = "Petal.Length",
#'     variables = list(arm = "Species", covariates = NULL),
#'     table_names = "unadj",
#'     conf_level = 0.95, var_labels = "Unadjusted comparison",
#'     .labels = c(lsmean = "Mean", lsmean_diff = "Difference in Means")
#'   ) %>%
#'   summarize_ancova(
#'     vars = "Petal.Length",
#'     variables = list(arm = "Species", covariates = c("Sepal.Length", "Sepal.Width")),
#'     table_names = "adj",
#'     conf_level = 0.95, var_labels = "Adjusted comparison (covariates: Sepal.Length and Sepal.Width)"
#'   ) %>%
#'   build_table(iris)
#'
#' @export
#' @order 2
summarize_ancova <- function(lyt,
                             vars,
                             variables,
                             conf_level,
                             interaction_y = FALSE,
                             interaction_item = NULL,
                             var_labels,
                             na_str = default_na_str(),
                             nested = TRUE,
                             ...,
                             show_labels = "visible",
                             table_names = vars,
                             .stats = NULL,
                             .formats = NULL,
                             .labels = NULL,
                             .indent_mods = NULL) {
  extra_args <- list(
    variables = variables, conf_level = conf_level, interaction_y = interaction_y,
    interaction_item = interaction_item, ...
  )

  afun <- make_afun(
    a_ancova,
    interaction_y = interaction_y,
    interaction_item = interaction_item,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Counting Missed Doses
#'
#' @description `r lifecycle::badge("stable")`
#'
#' These are specific functions to count patients with missed doses. The difference to [count_cumulative()] is
#' mainly the special labels.
#'
#' @inheritParams s_count_cumulative
#' @inheritParams argument_convention
#' @param thresholds (vector of `count`)\cr number of missed doses the patients at least had.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_missed_doses")`
#'   to see available statistics for this function.
#'
#' @seealso Relevant description function [d_count_missed_doses()].
#'
#' @name count_missed_doses
#' @order 1
NULL

#' @describeIn count_missed_doses Statistics function to count non-missing values.
#'
#' @return
#' * `s_count_nonmissing()` returns the statistic `n` which is the count of non-missing values in `x`.
#'
#' @keywords internal
s_count_nonmissing <- function(x) {
  list(n = n_available(x))
}

#' Description Function that Calculates Labels for [s_count_missed_doses()].
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams s_count_missed_doses
#'
#' @return [d_count_missed_doses()] returns a named `character` vector with the labels.
#'
#' @seealso [s_count_missed_doses()]
#'
#' @export
d_count_missed_doses <- function(thresholds) {
  paste0("At least ", thresholds, " missed dose", ifelse(thresholds > 1, "s", ""))
}

#' @describeIn count_missed_doses Statistics function to count patients with missed doses.
#'
#' @return
#' * `s_count_missed_doses()` returns the statistics `n` and `count_fraction` with one element for each threshold.
#'
#' @keywords internal
s_count_missed_doses <- function(x,
                                 thresholds,
                                 .N_col) { # nolint
  stat <- s_count_cumulative(
    x = x,
    thresholds = thresholds,
    lower_tail = FALSE,
    include_eq = TRUE,
    .N_col = .N_col
  )
  labels <- d_count_missed_doses(thresholds)
  for (i in seq_along(stat$count_fraction)) {
    stat$count_fraction[[i]] <- formatters::with_label(stat$count_fraction[[i]], label = labels[i])
  }
  n_stat <- s_count_nonmissing(x)
  c(n_stat, stat)
}

#' @describeIn count_missed_doses Formatted analysis function which is used as `afun`
#'   in `count_missed_doses()`.
#'
#' @return
#' * `a_count_missed_doses()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_missed_doses <- make_afun(
  s_count_missed_doses,
  .formats = c(n = "xx", count_fraction = format_count_fraction)
)

#' @describeIn count_missed_doses Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_missed_doses()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_missed_doses()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' anl <- tern_ex_adsl %>%
#'   distinct(STUDYID, USUBJID, ARM) %>%
#'   mutate(
#'     PARAMCD = "TNDOSMIS",
#'     PARAM = "Total number of missed doses during study",
#'     AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE),
#'     AVALC = ""
#'   )
#'
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_missed_doses("AVAL", thresholds = c(1, 5, 10, 15), var_labels = "Missed Doses") %>%
#'   build_table(anl, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_missed_doses <- function(lyt,
                               vars,
                               thresholds,
                               var_labels = vars,
                               show_labels = "visible",
                               na_str = default_na_str(),
                               nested = TRUE,
                               ...,
                               table_names = vars,
                               .stats = NULL,
                               .formats = NULL,
                               .labels = NULL,
                               .indent_mods = NULL) {
  extra_args <- list(thresholds = thresholds, ...)

  afun <- make_afun(
    a_count_missed_doses,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )
  analyze(
    lyt = lyt,
    vars = vars,
    afun = afun,
    var_labels = var_labels,
    table_names = table_names,
    show_labels = show_labels,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Occurrence Counts
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions for analyzing frequencies and fractions of occurrences for patients with occurrence
#' data. Primary analysis variables are the dictionary terms. All occurrences are counted for total
#' counts. Multiple occurrences within patient at the lowest term level displayed in the table are
#' counted only once.
#'
#' @inheritParams argument_convention
#' @param drop (`flag`)\cr should non appearing occurrence levels be dropped from the resulting table.
#'   Note that in that case the remaining occurrence levels in the table are sorted alphabetically.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_occurrences")`
#'   to see available statistics for this function.
#'
#' @note By default, occurrences which don't appear in a given row split are dropped from the table and
#'   the occurrences in the table are sorted alphabetically per row split. Therefore, the corresponding layout
#'   needs to use `split_fun = drop_split_levels` in the `split_rows_by` calls. Use `drop = FALSE` if you would
#'   like to show all occurrences.
#'
#' @examples
#' library(dplyr)
#' df <- data.frame(
#'   USUBJID = as.character(c(
#'     1, 1, 2, 4, 4, 4,
#'     6, 6, 6, 7, 7, 8
#'   )),
#'   MHDECOD = c(
#'     "MH1", "MH2", "MH1", "MH1", "MH1", "MH3",
#'     "MH2", "MH2", "MH3", "MH1", "MH2", "MH4"
#'   ),
#'   ARM = rep(c("A", "B"), each = 6),
#'   SEX = c("F", "F", "M", "M", "M", "M", "F", "F", "F", "M", "M", "F")
#' )
#' df_adsl <- df %>%
#'   select(USUBJID, ARM) %>%
#'   unique()
#'
#' @name count_occurrences
#' @order 1
NULL

#' @describeIn count_occurrences Statistics function which counts number of patients that report an
#' occurrence.
#'
#' @param denom (`string`)\cr choice of denominator for patient proportions. Can be:
#'   - `N_col`: total number of patients in this column across rows
#'   - `n`: number of patients with any occurrences
#'
#' @return
#' * `s_count_occurrences()` returns a list with:
#'   * `count`: list of counts with one element per occurrence.
#'   * `count_fraction`: list of counts and fractions with one element per occurrence.
#'   * `fraction`: list of numerators and denominators with one element per occurrence.
#'
#' @examples
#' # Count unique occurrences per subject.
#' s_count_occurrences(
#'   df,
#'   .N_col = 4L,
#'   .df_row = df,
#'   .var = "MHDECOD",
#'   id = "USUBJID"
#' )
#'
#' @export
s_count_occurrences <- function(df,
                                denom = c("N_col", "n"),
                                .N_col, # nolint
                                .df_row,
                                drop = TRUE,
                                .var = "MHDECOD",
                                id = "USUBJID") {
  checkmate::assert_flag(drop)
  assert_df_with_variables(df, list(range = .var, id = id))
  checkmate::assert_count(.N_col)
  checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[id]], classes = c("factor", "character"))
  denom <- match.arg(denom)

  occurrences <- if (drop) {
    # Note that we don't try to preserve original level order here since a) that would required
    # more time to look up in large original levels and b) that would fail for character input variable.
    occurrence_levels <- sort(unique(.df_row[[.var]]))
    if (length(occurrence_levels) == 0) {
      stop(
        "no empty `.df_row` input allowed when `drop = TRUE`,",
        " please use `split_fun = drop_split_levels` in the `rtables` `split_rows_by` calls"
      )
    }
    factor(df[[.var]], levels = occurrence_levels)
  } else {
    df[[.var]]
  }
  ids <- factor(df[[id]])
  dn <- switch(denom,
    n = nlevels(ids),
    N_col = .N_col
  )
  has_occurrence_per_id <- table(occurrences, ids) > 0
  n_ids_per_occurrence <- as.list(rowSums(has_occurrence_per_id))
  list(
    count = n_ids_per_occurrence,
    count_fraction = lapply(
      n_ids_per_occurrence,
      function(i, denom) {
        if (i == 0 && denom == 0) {
          c(0, 0)
        } else {
          c(i, i / denom)
        }
      },
      denom = dn
    ),
    fraction = lapply(
      n_ids_per_occurrence,
      function(i, denom) c("num" = i, "denom" = denom),
      denom = dn
    )
  )
}

#' @describeIn count_occurrences Formatted analysis function which is used as `afun`
#'   in `count_occurrences()`.
#'
#' @return
#' * `a_count_occurrences()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_count_occurrences(
#'   df,
#'   .N_col = 4L,
#'   .df_row = df,
#'   .var = "MHDECOD",
#'   id = "USUBJID"
#' )
#'
#' @export
a_count_occurrences <- function(df,
                                labelstr = "",
                                id = "USUBJID",
                                denom = c("N_col", "n"),
                                drop = TRUE,
                                .N_col, # nolint
                                .var = NULL,
                                .df_row = NULL,
                                .stats = NULL,
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = NULL,
                                na_str = default_na_str()) {
  denom <- match.arg(denom)
  x_stats <- s_count_occurrences(
    df = df, denom = denom, .N_col = .N_col, .df_row = .df_row, drop = drop, .var = .var, id = id
  )
  if (is.null(unlist(x_stats))) {
    return(NULL)
  }
  x_lvls <- names(x_stats[[1]])

  # Fill in with formatting defaults if needed
  .stats <- get_stats("count_occurrences", stats_in = .stats)
  .formats <- get_formats_from_stats(.stats, .formats)
  .labels <- get_labels_from_stats(.stats, .labels, row_nms = x_lvls)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods, row_nms = x_lvls)

  if ("count_fraction_fixed_dp" %in% .stats) x_stats[["count_fraction_fixed_dp"]] <- x_stats[["count_fraction"]]
  x_stats <- x_stats[.stats]

  # Ungroup statistics with values for each level of x
  x_ungrp <- ungroup_stats(x_stats, .formats, list(), list())
  x_stats <- x_ungrp[["x"]]
  .formats <- x_ungrp[[".formats"]]

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = .labels,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .format_na_strs = na_str
  )
}

#' @describeIn count_occurrences Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_occurrences()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_occurrences()` to the table layout.
#'
#' @examples
#' # Create table layout
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_occurrences(vars = "MHDECOD", .stats = c("count_fraction"))
#'
#' # Apply table layout to data and produce `rtable` object
#' tbl <- lyt %>%
#'   build_table(df, alt_counts_df = df_adsl) %>%
#'   prune_table()
#'
#' tbl
#'
#' @export
#' @order 2
count_occurrences <- function(lyt,
                              vars,
                              id = "USUBJID",
                              drop = TRUE,
                              var_labels = vars,
                              show_labels = "hidden",
                              riskdiff = FALSE,
                              na_str = default_na_str(),
                              nested = TRUE,
                              ...,
                              table_names = vars,
                              .stats = "count_fraction_fixed_dp",
                              .formats = NULL,
                              .labels = NULL,
                              .indent_mods = NULL) {
  checkmate::assert_flag(riskdiff)

  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(id = id, drop = drop, ...)

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_occurrences" = a_count_occurrences),
        s_args = s_args
      )
    )
  }

  analyze(
    lyt = lyt,
    vars = vars,
    afun = ifelse(isFALSE(riskdiff), a_count_occurrences, afun_riskdiff),
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' @describeIn count_occurrences Layout-creating function which can take content function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_occurrences()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
#'   containing the statistics from `s_count_occurrences()` to the table layout.
#'
#' @examples
#' # Layout creating function with custom format.
#' basic_table() %>%
#'   add_colcounts() %>%
#'   split_rows_by("SEX", child_labels = "visible") %>%
#'   summarize_occurrences(
#'     var = "MHDECOD",
#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' @export
#' @order 3
summarize_occurrences <- function(lyt,
                                  var,
                                  id = "USUBJID",
                                  drop = TRUE,
                                  riskdiff = FALSE,
                                  na_str = default_na_str(),
                                  ...,
                                  .stats = "count_fraction_fixed_dp",
                                  .formats = NULL,
                                  .indent_mods = NULL,
                                  .labels = NULL) {
  checkmate::assert_flag(riskdiff)

  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(id = id, drop = drop, ...)

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_occurrences" = a_count_occurrences),
        s_args = s_args
      )
    )
  }

  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = ifelse(isFALSE(riskdiff), a_count_occurrences, afun_riskdiff),
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Compare Variables Between Groups
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Comparison with a reference group for different `x` objects.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("analyze_vars_numeric")` to see
#'   statistics available for numeric variables, and `get_stats("analyze_vars_counts")` for statistics available
#'   for non-numeric variables.
#'
#' @note
#' * For factor variables, `denom` for factor proportions can only be `n` since the purpose is to compare proportions
#'   between columns, therefore a row-based proportion would not make sense. Proportion based on `N_col` would
#'   be difficult since we use counts for the chi-squared test statistic, therefore missing values should be accounted
#'   for as explicit factor levels.
#' * If factor variables contain `NA`, these `NA` values are excluded by default. To include `NA` values
#'   set `na.rm = FALSE` and missing values will be displayed as an `NA` level. Alternatively, an explicit
#'   factor level can be defined for `NA` values during pre-processing via [df_explicit_na()] - the
#'   default `na_level` (`"<Missing>"`) will also be excluded when `na.rm` is set to `TRUE`.
#' * For character variables, automatic conversion to factor does not guarantee that the table
#'   will be generated correctly. In particular for sparse tables this very likely can fail.
#'   Therefore it is always better to manually convert character variables to factors during pre-processing.
#' * For `compare_vars()`, the column split must define a reference group via `ref_group` so that the comparison
#'   is well defined.
#'
#' @seealso [s_summary()] which is used internally to compute a summary within `s_compare()`, and [a_summary()]
#'   which is used (with `compare = TRUE`) as the analysis function for `compare_vars()`.
#'
#' @name compare_variables
#' @include analyze_variables.R
#' @order 1
NULL

#' @describeIn compare_variables S3 generic function to produce a comparison summary.
#'
#' @return
#' * `s_compare()` returns output of [s_summary()] and comparisons versus the reference group in the form of p-values.
#'
#' @export
s_compare <- function(x,
                      .ref_group,
                      .in_ref_col,
                      ...) {
  UseMethod("s_compare", x)
}

#' @describeIn compare_variables Method for `numeric` class. This uses the standard t-test
#'   to calculate the p-value.
#'
#' @method s_compare numeric
#'
#' @examples
#' # `s_compare.numeric`
#'
#' ## Usual case where both this and the reference group vector have more than 1 value.
#' s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE)
#'
#' ## If one group has not more than 1 value, then p-value is not calculated.
#' s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE)
#'
#' ## Empty numeric does not fail, it returns NA-filled items and no p-value.
#' s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE)
#'
#' @export
s_compare.numeric <- function(x,
                              .ref_group,
                              .in_ref_col,
                              ...) {
  checkmate::assert_numeric(x)
  checkmate::assert_numeric(.ref_group)
  checkmate::assert_flag(.in_ref_col)

  y <- s_summary.numeric(x = x, ...)

  y$pval <- if (!.in_ref_col && n_available(x) > 1 && n_available(.ref_group) > 1) {
    stats::t.test(x, .ref_group)$p.value
  } else {
    character()
  }

  y
}

#' @describeIn compare_variables Method for `factor` class. This uses the chi-squared test
#'   to calculate the p-value.
#'
#' @param denom (`string`)\cr choice of denominator for factor proportions,
#'   can only be `n` (number of values in this row and column intersection).
#'
#' @method s_compare factor
#'
#' @examples
#' # `s_compare.factor`
#'
#' ## Basic usage:
#' x <- factor(c("a", "a", "b", "c", "a"))
#' y <- factor(c("a", "b", "c"))
#' s_compare(x = x, .ref_group = y, .in_ref_col = FALSE)
#'
#' ## Management of NA values.
#' x <- explicit_na(factor(c("a", "a", "b", "c", "a", NA, NA)))
#' y <- explicit_na(factor(c("a", "b", "c", NA)))
#' s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE)
#' s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE)
#'
#' @export
s_compare.factor <- function(x,
                             .ref_group,
                             .in_ref_col,
                             denom = "n",
                             na.rm = TRUE, # nolint
                             ...) {
  checkmate::assert_flag(.in_ref_col)
  assert_valid_factor(x)
  assert_valid_factor(.ref_group)
  denom <- match.arg(denom)

  y <- s_summary.factor(
    x = x,
    denom = denom,
    na.rm = na.rm,
    ...
  )

  if (na.rm) {
    x <- x[!is.na(x)] %>% fct_discard("<Missing>")
    .ref_group <- .ref_group[!is.na(.ref_group)] %>% fct_discard("<Missing>")
  } else {
    x <- x %>% explicit_na(label = "NA")
    .ref_group <- .ref_group %>% explicit_na(label = "NA")
  }

  if ("NA" %in% levels(x)) levels(.ref_group) <- c(levels(.ref_group), "NA")
  checkmate::assert_factor(x, levels = levels(.ref_group), min.levels = 2)

  y$pval_counts <- if (!.in_ref_col && length(x) > 0 && length(.ref_group) > 0) {
    tab <- rbind(table(x), table(.ref_group))
    res <- suppressWarnings(stats::chisq.test(tab))
    res$p.value
  } else {
    character()
  }

  y
}

#' @describeIn compare_variables Method for `character` class. This makes an automatic
#'   conversion to `factor` (with a warning) and then forwards to the method for factors.
#'
#' @param verbose (`logical`)\cr Whether warnings and messages should be printed. Mainly used
#'   to print out information about factor casting. Defaults to `TRUE`.
#'
#' @method s_compare character
#'
#' @examples
#' # `s_compare.character`
#'
#' ## Basic usage:
#' x <- c("a", "a", "b", "c", "a")
#' y <- c("a", "b", "c")
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = "x", verbose = FALSE)
#'
#' ## Note that missing values handling can make a large difference:
#' x <- c("a", "a", "b", "c", "a", NA)
#' y <- c("a", "b", "c", rep(NA, 20))
#' s_compare(x,
#'   .ref_group = y, .in_ref_col = FALSE,
#'   .var = "x", verbose = FALSE
#' )
#' s_compare(x,
#'   .ref_group = y, .in_ref_col = FALSE, .var = "x",
#'   na.rm = FALSE, verbose = FALSE
#' )
#'
#' @export
s_compare.character <- function(x,
                                .ref_group,
                                .in_ref_col,
                                denom = "n",
                                na.rm = TRUE, # nolint
                                .var,
                                verbose = TRUE,
                                ...) {
  x <- as_factor_keep_attributes(x, verbose = verbose)
  .ref_group <- as_factor_keep_attributes(.ref_group, verbose = verbose)
  s_compare(
    x = x,
    .ref_group = .ref_group,
    .in_ref_col = .in_ref_col,
    denom = denom,
    na.rm = na.rm,
    ...
  )
}

#' @describeIn compare_variables Method for `logical` class. A chi-squared test
#'   is used. If missing values are not removed, then they are counted as `FALSE`.
#'
#' @method s_compare logical
#'
#' @examples
#' # `s_compare.logical`
#'
#' ## Basic usage:
#' x <- c(TRUE, FALSE, TRUE, TRUE)
#' y <- c(FALSE, FALSE, TRUE)
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE)
#'
#' ## Management of NA values.
#' x <- c(NA, TRUE, FALSE)
#' y <- c(NA, NA, NA, NA, FALSE)
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE)
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE)
#'
#' @export
s_compare.logical <- function(x,
                              .ref_group,
                              .in_ref_col,
                              na.rm = TRUE, # nolint
                              denom = "n",
                              ...) {
  denom <- match.arg(denom)

  y <- s_summary.logical(
    x = x,
    na.rm = na.rm,
    denom = denom,
    ...
  )

  if (na.rm) {
    x <- stats::na.omit(x)
    .ref_group <- stats::na.omit(.ref_group)
  } else {
    x[is.na(x)] <- FALSE
    .ref_group[is.na(.ref_group)] <- FALSE
  }

  y$pval_counts <- if (!.in_ref_col && length(x) > 0 && length(.ref_group) > 0) {
    x <- factor(x, levels = c(TRUE, FALSE))
    .ref_group <- factor(.ref_group, levels = c(TRUE, FALSE))
    tbl <- rbind(table(x), table(.ref_group))
    suppressWarnings(prop_chisq(tbl))
  } else {
    character()
  }

  y
}

#' @describeIn compare_variables Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @param ... arguments passed to `s_compare()`.
#' @param .indent_mods (named `vector` of `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @return
#' * `compare_vars()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_compare()` to the table layout.
#'
#' @examples
#' # `compare_vars()` in `rtables` pipelines
#'
#' ## Default output within a `rtables` pipeline.
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", ref_group = "ARM B") %>%
#'   compare_vars(c("AGE", "SEX"))
#' build_table(lyt, tern_ex_adsl)
#'
#' ## Select and format statistics output.
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", ref_group = "ARM C") %>%
#'   compare_vars(
#'     vars = "AGE",
#'     .stats = c("mean_sd", "pval"),
#'     .formats = c(mean_sd = "xx.x, xx.x"),
#'     .labels = c(mean_sd = "Mean, SD")
#'   )
#' build_table(lyt, df = tern_ex_adsl)
#'
#' @export
#' @order 2
compare_vars <- function(lyt,
                         vars,
                         var_labels = vars,
                         na_str = default_na_str(),
                         nested = TRUE,
                         ...,
                         na.rm = TRUE, # nolint
                         show_labels = "default",
                         table_names = vars,
                         section_div = NA_character_,
                         .stats = c("n", "mean_sd", "count_fraction", "pval"),
                         .formats = NULL,
                         .labels = NULL,
                         .indent_mods = NULL) {
  extra_args <- list(.stats = .stats, na.rm = na.rm, na_str = na_str, compare = TRUE, ...)

  if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
  if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
  if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods

  analyze(
    lyt = lyt,
    vars = vars,
    var_labels = var_labels,
    afun = a_summary,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    inclNAs = TRUE,
    show_labels = show_labels,
    table_names = table_names,
    section_div = section_div
  )
}

#' Tabulate Biomarker Effects on Binary Response by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Tabulate the estimated effects of multiple continuous biomarker variables
#' on a binary response endpoint across population subgroups.
#'
#' @inheritParams argument_convention
#' @param df (`data.frame`)\cr containing all analysis variables, as returned by
#'   [extract_rsp_biomarkers()].
#' @param vars (`character`)\cr the names of statistics to be reported among:
#'   * `n_tot`: Total number of patients per group.
#'   * `n_rsp`: Total number of responses per group.
#'   * `prop`: Total response proportion per group.
#'   * `or`: Odds ratio.
#'   * `ci`: Confidence interval of odds ratio.
#'   * `pval`: p-value of the effect.
#'   Note, the statistics `n_tot`, `or` and `ci` are required.
#'
#' @return An `rtables` table summarizing biomarker effects on binary response by subgroup.
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. The tables are then typically used as input for forest plots.
#'
#' @note In contrast to [tabulate_rsp_subgroups()] this tabulation function does
#'   not start from an input layout `lyt`. This is because internally the table is
#'   created by combining multiple subtables.
#'
#' @seealso [h_tab_rsp_one_biomarker()] which is used internally, [extract_rsp_biomarkers()].
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   mutate(rsp = AVALC == "CR")
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' df <- extract_rsp_biomarkers(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   data = adrs_f
#' )
#'
#' \donttest{
#' ## Table with default columns.
#' tabulate_rsp_biomarkers(df)
#'
#' ## Table with a manually chosen set of columns: leave out "pval", reorder.
#' tab <- tabulate_rsp_biomarkers(
#'   df = df,
#'   vars = c("n_rsp", "ci", "n_tot", "prop", "or")
#' )
#'
#' ## Finally produce the forest plot.
#' g_forest(tab, xlim = c(0.7, 1.4))
#' }
#'
#' @export
#' @name response_biomarkers_subgroups
tabulate_rsp_biomarkers <- function(df,
                                    vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval"),
                                    na_str = default_na_str(),
                                    .indent_mods = 0L) {
  checkmate::assert_data_frame(df)
  checkmate::assert_character(df$biomarker)
  checkmate::assert_character(df$biomarker_label)
  checkmate::assert_subset(vars, get_stats("tabulate_rsp_biomarkers"))

  df_subs <- split(df, f = df$biomarker)
  tabs <- lapply(df_subs, FUN = function(df_sub) {
    tab_sub <- h_tab_rsp_one_biomarker(
      df = df_sub,
      vars = vars,
      na_str = na_str,
      .indent_mods = .indent_mods
    )
    # Insert label row as first row in table.
    label_at_path(tab_sub, path = row_paths(tab_sub)[[1]][1]) <- df_sub$biomarker_label[1]
    tab_sub
  })
  result <- do.call(rbind, tabs)

  n_id <- grep("n_tot", vars)
  or_id <- match("or", vars)
  ci_id <- match("ci", vars)
  structure(
    result,
    forest_header = paste0(c("Lower", "Higher"), "\nBetter"),
    col_x = or_id,
    col_ci = ci_id,
    col_symbol_size = n_id
  )
}

#' Prepares Response Data Estimates for Multiple Biomarkers in a Single Data Frame
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares estimates for number of responses, patients and overall response rate,
#' as well as odds ratio estimates, confidence intervals and p-values,
#' for multiple biomarkers across population subgroups in a single data frame.
#' `variables` corresponds to the names of variables found in `data`, passed as a
#' named list and requires elements `rsp` and `biomarkers` (vector of continuous
#' biomarker variables) and optionally `covariates`, `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams response_subgroups
#' @param control (named `list`)\cr controls for the response definition and the
#'   confidence level produced by [control_logistic()].
#'
#' @return A `data.frame` with columns `biomarker`, `biomarker_label`, `n_tot`, `n_rsp`,
#'   `prop`, `or`, `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`,
#'   `var_label`, and `row_type`.
#'
#' @note You can also specify a continuous variable in `rsp` and then use the
#'   `response_definition` control to convert that internally to a logical
#'   variable reflecting binary response.
#'
#' @seealso [h_logistic_mult_cont_df()] which is used internally.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   mutate(rsp = AVALC == "CR")
#'
#' # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
#' # in logistic regression models with one covariate `RACE`. The subgroups
#' # are defined by the levels of `BMRKR2`.
#' df <- extract_rsp_biomarkers(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   data = adrs_f
#' )
#' df
#'
#' # Here we group the levels of `BMRKR2` manually, and we add a stratification
#' # variable `STRATA1`. We also here use a continuous variable `EOSDY`
#' # which is then binarized internally (response is defined as this variable
#' # being larger than 750).
#' df_grouped <- extract_rsp_biomarkers(
#'   variables = list(
#'     rsp = "EOSDY",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     subgroups = "BMRKR2",
#'     strata = "STRATA1"
#'   ),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   ),
#'   control = control_logistic(
#'     response_definition = "I(response > 750)"
#'   )
#' )
#' df_grouped
#'
#' @export
extract_rsp_biomarkers <- function(variables,
                                   data,
                                   groups_lists = list(),
                                   control = control_logistic(),
                                   label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_rsp_biomarkers() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  assert_list_of_variables(variables)
  checkmate::assert_string(variables$rsp)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_string(label_all)

  # Start with all patients.
  result_all <- h_logistic_mult_cont_df(
    variables = variables,
    data = data,
    control = control
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"
  if (is.null(variables$subgroups)) {
    # Only return result for all patients.
    result_all
  } else {
    # Add subgroups results.
    l_data <- h_split_by_subgroups(
      data,
      variables$subgroups,
      groups_lists = groups_lists
    )
    l_result <- lapply(l_data, function(grp) {
      result <- h_logistic_mult_cont_df(
        variables = variables,
        data = grp$df,
        control = control
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"
    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' Additional Assertions for `checkmate`
#'
#' Additional assertion functions which can be used together with the `checkmate` package.
#'
#' @inheritParams checkmate::assert_factor
#' @param x (`any`)\cr object to test.
#' @param df (`data.frame`)\cr data set to test.
#' @param variables (named `list` of `character`)\cr list of variables to test.
#' @param include_boundaries (`logical`)\cr whether to include boundaries when testing
#'   for proportions.
#' @param na_level (`character`)\cr the string you have been using to represent NA or
#'   missing data. For `NA` values please consider using directly [is.na()] or
#'   similar approaches.
#'
#' @return Nothing if assertion passes, otherwise prints the error message.
#'
#' @name assertions
NULL

check_list_of_variables <- function(x) {
  # drop NULL elements in list
  x <- Filter(Negate(is.null), x)

  res <- checkmate::check_list(x,
    names = "named",
    min.len = 1,
    any.missing = FALSE,
    types = "character"
  )
  # no empty strings allowed
  if (isTRUE(res)) {
    res <- checkmate::check_character(unlist(x), min.chars = 1)
  }
  return(res)
}
#' @describeIn assertions Checks whether `x` is a valid list of variable names.
#'   `NULL` elements of the list `x` are dropped with `Filter(Negate(is.null), x)`.
#'
#' @keywords internal
assert_list_of_variables <- checkmate::makeAssertionFunction(check_list_of_variables)

check_df_with_variables <- function(df, variables, na_level = NULL) {
  checkmate::assert_data_frame(df)
  assert_list_of_variables(variables)

  # flag for equal variables and column names
  err_flag <- all(unlist(variables) %in% colnames(df))
  checkmate::assert_flag(err_flag)

  if (isFALSE(err_flag)) {
    vars <- setdiff(unlist(variables), colnames(df))
    return(paste(
      deparse(substitute(df)),
      "does not contain all specified variables as column names. Missing from dataframe:",
      paste(vars, collapse = ", ")
    ))
  }
  # checking if na_level is present and in which column
  if (!is.null(na_level)) {
    checkmate::assert_string(na_level)
    res <- unlist(lapply(as.list(df)[unlist(variables)], function(x) any(x == na_level)))
    if (any(res)) {
      return(paste0(
        deparse(substitute(df)), " contains explicit na_level (", na_level,
        ") in the following columns: ", paste0(unlist(variables)[res],
          collapse = ", "
        )
      ))
    }
  }
  return(TRUE)
}
#' @describeIn assertions Check whether `df` is a data frame with the analysis `variables`.
#'   Please notice how this produces an error when not all variables are present in the
#'   data.frame while the opposite is not required.
#'
#' @keywords internal
assert_df_with_variables <- checkmate::makeAssertionFunction(check_df_with_variables)

check_valid_factor <- function(x,
                               min.levels = 1, # nolint
                               max.levels = NULL, # nolint
                               null.ok = TRUE, # nolint
                               any.missing = TRUE, # nolint
                               n.levels = NULL, # nolint
                               len = NULL) {
  # checks on levels insertion
  checkmate::assert_int(min.levels, lower = 1)

  # main factor check
  res <- checkmate::check_factor(x,
    min.levels = min.levels,
    null.ok = null.ok,
    max.levels = max.levels,
    any.missing = any.missing,
    n.levels = n.levels
  )

  # no empty strings allowed
  if (isTRUE(res)) {
    res <- checkmate::check_character(levels(x), min.chars = 1)
  }

  return(res)
}
#' @describeIn assertions Check whether `x` is a valid factor (i.e. has levels and no empty
#'   string levels). Note that `NULL` and `NA` elements are allowed.
#'
#' @keywords internal
assert_valid_factor <- checkmate::makeAssertionFunction(check_valid_factor)


check_df_with_factors <- function(df,
                                  variables,
                                  min.levels = 1, # nolint
                                  max.levels = NULL, # nolint
                                  any.missing = TRUE, # nolint
                                  na_level = NULL) {
  res <- check_df_with_variables(df, variables, na_level)
  # checking if all the columns specified by variables are valid factors
  if (isTRUE(res)) {
    # searching the data.frame with selected columns (variables) as a list
    res <- lapply(
      X = as.list(df)[unlist(variables)],
      FUN = check_valid_factor,
      min.levels = min.levels,
      max.levels = max.levels,
      any.missing = any.missing
    )
    res_lo <- unlist(vapply(res, Negate(isTRUE), logical(1)))
    if (any(res_lo)) {
      return(paste0(
        deparse(substitute(df)), " does not contain only factor variables among:",
        "\n* Column `", paste0(unlist(variables)[res_lo],
          "` of the data.frame -> ", res[res_lo],
          collapse = "\n* "
        )
      ))
    } else {
      res <- TRUE
    }
  }
  return(res)
}
#' @describeIn assertions Check whether `df` is a data frame where the analysis `variables`
#'   are all factors. Note that the creation of `NA` by direct call of `factor()` will
#'   trim `NA` levels out of the vector list itself.
#'
#' @keywords internal
assert_df_with_factors <- checkmate::makeAssertionFunction(check_df_with_factors)

#' @describeIn assertions Check whether `x` is a proportion: number between 0 and 1.
#'
#' @keywords internal
assert_proportion_value <- function(x, include_boundaries = FALSE) {
  checkmate::assert_number(x, lower = 0, upper = 1)
  checkmate::assert_flag(include_boundaries)
  if (isFALSE(include_boundaries)) {
    checkmate::assert_true(x > 0)
    checkmate::assert_true(x < 1)
  }
}

#' Combine Factor Levels
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Combine specified old factor Levels in a single new level.
#'
#' @param x factor
#' @param levels level names to be combined
#' @param new_level name of new level
#'
#' @return A `factor` with the new levels.
#'
#' @examples
#' x <- factor(letters[1:5], levels = letters[5:1])
#' combine_levels(x, levels = c("a", "b"))
#'
#' combine_levels(x, c("e", "b"))
#'
#' @export
combine_levels <- function(x, levels, new_level = paste(levels, collapse = "/")) {
  checkmate::assert_factor(x)
  checkmate::assert_subset(levels, levels(x))

  lvls <- levels(x)

  lvls[lvls %in% levels] <- new_level

  levels(x) <- lvls

  x
}

#' Conversion of a Vector to a Factor
#'
#' Converts `x` to a factor and keeps its attributes. Warns appropriately such that the user
#' can decide whether they prefer converting to factor manually (e.g. for full control of
#' factor levels).
#'
#' @param x (`atomic`)\cr object to convert.
#' @param x_name (`string`)\cr name of `x`.
#' @param na_level (`string`)\cr the explicit missing level which should be used when converting a character vector.
#' @param verbose defaults to `TRUE`. It prints out warnings and messages.
#'
#' @return A `factor` with same attributes (except class) as `x`. Does not modify `x` if already a `factor`.
#'
#' @keywords internal
as_factor_keep_attributes <- function(x,
                                      x_name = deparse(substitute(x)),
                                      na_level = "<Missing>",
                                      verbose = TRUE) {
  checkmate::assert_atomic(x)
  checkmate::assert_string(x_name)
  checkmate::assert_string(na_level)
  checkmate::assert_flag(verbose)
  if (is.factor(x)) {
    return(x)
  }
  x_class <- class(x)[1]
  if (verbose) {
    warning(paste(
      "automatically converting", x_class, "variable", x_name,
      "to factor, better manually convert to factor to avoid failures"
    ))
  }
  if (identical(length(x), 0L)) {
    warning(paste(
      x_name, "has length 0, this can lead to tabulation failures, better convert to factor"
    ))
  }
  if (is.character(x)) {
    x_no_na <- explicit_na(sas_na(x), label = na_level)
    if (any(na_level %in% x_no_na)) {
      do.call(
        structure,
        c(
          list(.Data = forcats::fct_relevel(x_no_na, na_level, after = Inf)),
          attributes(x)
        )
      )
    } else {
      do.call(structure, c(list(.Data = as.factor(x)), attributes(x)))
    }
  } else {
    do.call(structure, c(list(.Data = as.factor(x)), attributes(x)))
  }
}

#' Labels for Bins in Percent
#'
#' This creates labels for quantile based bins in percent. This assumes the right-closed
#' intervals as produced by [cut_quantile_bins()].
#'
#' @param probs (`proportion` vector)\cr the probabilities identifying the quantiles.
#'   This is a sorted vector of unique `proportion` values, i.e. between 0 and 1, where
#'   the boundaries 0 and 1 must not be included.
#' @param digits (`integer`)\cr number of decimal places to round the percent numbers.
#'
#' @return A `character` vector with labels in the format `[0%,20%]`, `(20%,50%]`, etc.
#'
#' @keywords internal
bins_percent_labels <- function(probs,
                                digits = 0) {
  if (isFALSE(0 %in% probs)) probs <- c(0, probs)
  if (isFALSE(1 %in% probs)) probs <- c(probs, 1)
  checkmate::assert_numeric(probs, lower = 0, upper = 1, unique = TRUE, sorted = TRUE)
  percent <- round(probs * 100, digits = digits)
  left <- paste0(utils::head(percent, -1), "%")
  right <- paste0(utils::tail(percent, -1), "%")
  without_left_bracket <- paste0(left, ",", right, "]")
  with_left_bracket <- paste0("[", utils::head(without_left_bracket, 1))
  if (length(without_left_bracket) > 1) {
    with_left_bracket <- c(
      with_left_bracket,
      paste0("(", utils::tail(without_left_bracket, -1))
    )
  }
  with_left_bracket
}

#' Cutting Numeric Vector into Empirical Quantile Bins
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This cuts a numeric vector into sample quantile bins.
#'
#' @inheritParams bins_percent_labels
#' @param x (`numeric`)\cr the continuous variable values which should be cut into
#'   quantile bins. This may contain `NA` values, which are then
#'   not used for the quantile calculations, but included in the return vector.
#' @param labels (`character`)\cr the unique labels for the quantile bins. When there are `n`
#'   probabilities in `probs`, then this must be `n + 1` long.
#' @param type (`integer`)\cr type of quantiles to use, see [stats::quantile()] for details.
#' @param ordered (`flag`)\cr should the result be an ordered factor.
#'
#' @return A `factor` variable with appropriately-labeled bins as levels.
#'
#' @note Intervals are closed on the right side. That is, the first bin is the interval
#'   `[-Inf, q1]` where `q1` is the first quantile, the second bin is then `(q1, q2]`, etc.,
#'   and the last bin is `(qn, +Inf]` where `qn` is the last quantile.
#'
#' @examples
#' # Default is to cut into quartile bins.
#' cut_quantile_bins(cars$speed)
#'
#' # Use custom quantiles.
#' cut_quantile_bins(cars$speed, probs = c(0.1, 0.2, 0.6, 0.88))
#'
#' # Use custom labels.
#' cut_quantile_bins(cars$speed, labels = paste0("Q", 1:4))
#'
#' # NAs are preserved in result factor.
#' ozone_binned <- cut_quantile_bins(airquality$Ozone)
#' which(is.na(ozone_binned))
#' # So you might want to make these explicit.
#' explicit_na(ozone_binned)
#'
#' @export
cut_quantile_bins <- function(x,
                              probs = c(0.25, 0.5, 0.75),
                              labels = NULL,
                              type = 7,
                              ordered = TRUE) {
  checkmate::assert_flag(ordered)
  checkmate::assert_numeric(x)
  if (isFALSE(0 %in% probs)) probs <- c(0, probs)
  if (isFALSE(1 %in% probs)) probs <- c(probs, 1)
  checkmate::assert_numeric(probs, lower = 0, upper = 1, unique = TRUE, sorted = TRUE)
  if (is.null(labels)) labels <- bins_percent_labels(probs)
  checkmate::assert_character(labels, len = length(probs) - 1, any.missing = FALSE, unique = TRUE)

  if (all(is.na(x))) {
    # Early return if there are only NAs in input.
    return(factor(x, ordered = ordered, levels = labels))
  }

  quantiles <- stats::quantile(
    x,
    probs = probs,
    type = type,
    na.rm = TRUE
  )

  checkmate::assert_numeric(quantiles, unique = TRUE)

  cut(
    x,
    breaks = quantiles,
    labels = labels,
    ordered_result = ordered,
    include.lowest = TRUE,
    right = TRUE
  )
}

#' Discard Certain Levels from a Factor
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This discards the observations as well as the levels specified from a factor.
#'
#' @param x (`factor`)\cr the original factor.
#' @param discard (`character`)\cr which levels to discard.
#'
#' @return A modified `factor` with observations as well as levels from `discard` dropped.
#'
#' @examples
#' fct_discard(factor(c("a", "b", "c")), "c")
#'
#' @export
fct_discard <- function(x, discard) {
  checkmate::assert_factor(x)
  checkmate::assert_character(discard, any.missing = FALSE)
  new_obs <- x[!(x %in% discard)]
  new_levels <- setdiff(levels(x), discard)
  factor(new_obs, levels = new_levels)
}

#' Insertion of Explicit Missings in a Factor
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This inserts explicit missings in a factor based on a condition. Additionally,
#' existing `NA` values will be explicitly converted to given `na_level`.
#'
#' @param x (`factor`)\cr the original factor.
#' @param condition (`logical`)\cr where to insert missings.
#' @param na_level (`string`)\cr which level to use for missings.
#'
#' @return A modified `factor` with inserted and existing `NA` converted to `na_level`.
#'
#' @seealso [forcats::fct_na_value_to_level()] which is used internally.
#'
#' @examples
#' fct_explicit_na_if(factor(c("a", "b", NA)), c(TRUE, FALSE, FALSE))
#'
#' @export
fct_explicit_na_if <- function(x, condition, na_level = "<Missing>") {
  checkmate::assert_factor(x, len = length(condition))
  checkmate::assert_logical(condition)
  x[condition] <- NA
  x <- forcats::fct_na_value_to_level(x, level = na_level)
  forcats::fct_drop(x, only = na_level)
}

#' Collapsing of Factor Levels and Keeping Only Those New Group Levels
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This collapses levels and only keeps those new group levels, in the order provided.
#' The returned factor has levels in the order given, with the possible missing level last (this will
#' only be included if there are missing values).
#'
#' @param .f (`factor` or `character`)\cr original vector.
#' @param ... (named `character` vectors)\cr levels in each vector provided will be collapsed into
#'   the new level given by the respective name.
#' @param .na_level (`string`)\cr which level to use for other levels, which should be missing in the
#'   new factor. Note that this level must not be contained in the new levels specified in `...`.
#'
#' @return A modified `factor` with collapsed levels. Values and levels which are not included
#'   in the given `character` vector input will be set to the missing level `.na_level`.
#'
#' @note Any existing `NA`s in the input vector will not be replaced by the missing level. If needed,
#'   [explicit_na()] can be called separately on the result.
#'
#' @seealso [forcats::fct_collapse()], [forcats::fct_relevel()] which are used internally.
#'
#' @examples
#' fct_collapse_only(factor(c("a", "b", "c", "d")), TRT = "b", CTRL = c("c", "d"))
#'
#' @export
fct_collapse_only <- function(.f, ..., .na_level = "<Missing>") {
  new_lvls <- names(list(...))
  if (checkmate::test_subset(.na_level, new_lvls)) {
    stop(paste0(".na_level currently set to '", .na_level, "' must not be contained in the new levels"))
  }
  x <- forcats::fct_collapse(.f, ..., other_level = .na_level)
  do.call(forcats::fct_relevel, args = c(list(.f = x), as.list(new_lvls)))
}

#' Ungroup Non-Numeric Statistics
#'
#' Ungroups grouped non-numeric statistics within input vectors `.formats`, `.labels`, and `.indent_mods`.
#'
#' @inheritParams argument_convention
#' @param x  (`named list` of `numeric`)\cr list of numeric statistics containing the statistics to ungroup.
#'
#' @return A `list` with modified elements `x`, `.formats`, `.labels`, and `.indent_mods`.
#'
#' @seealso [a_summary()] which uses this function internally.
#'
#' @keywords internal
ungroup_stats <- function(x,
                          .formats,
                          .labels,
                          .indent_mods) {
  checkmate::assert_list(x)
  empty_pval <- "pval" %in% names(x) && length(x[["pval"]]) == 0
  empty_pval_counts <- "pval_counts" %in% names(x) && length(x[["pval_counts"]]) == 0
  x <- unlist(x, recursive = FALSE)

  # If p-value is empty it is removed by unlist and needs to be re-added
  if (empty_pval) x[["pval"]] <- character()
  if (empty_pval_counts) x[["pval_counts"]] <- character()
  .stats <- names(x)

  # Ungroup stats
  .formats <- lapply(.stats, function(x) {
    .formats[[if (!grepl("\\.", x)) x else regmatches(x, regexpr("\\.", x), invert = TRUE)[[1]][1]]]
  })
  .indent_mods <- sapply(.stats, function(x) {
    .indent_mods[[if (!grepl("\\.", x)) x else regmatches(x, regexpr("\\.", x), invert = TRUE)[[1]][1]]]
  })
  .labels <- sapply(.stats, function(x) {
    if (!grepl("\\.", x)) .labels[[x]] else regmatches(x, regexpr("\\.", x), invert = TRUE)[[1]][2]
  })

  list(
    x = x,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
}

#' Horizontal Waterfall Plot
#'
#' This basic waterfall plot visualizes a quantity `height` ordered by value with some markup.
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param height (`numeric``)\cr vector containing values to be plotted as the waterfall bars.
#' @param id (`character`)\cr vector containing IDs to use as the x-axis label for the waterfall bars.
#' @param col (`character`)\cr colors.
#' @param col_var (`factor`, `character` or `NULL`)\cr categorical variable for bar coloring. `NULL` by default.
#' @param xlab (`character`)\cr x label. Default is `"ID"`.
#' @param ylab (`character`)\cr y label. Default is `"Value"`.
#' @param title (`character`)\cr text to be displayed as plot title.
#' @param col_legend_title (`character`)\cr text to be displayed as legend title.
#'
#' @return A `ggplot` waterfall plot.
#'
#' @examples
#' library(dplyr)
#' library(nestcolor)
#'
#' g_waterfall(height = c(3, 5, -1), id = letters[1:3])
#'
#' g_waterfall(
#'   height = c(3, 5, -1),
#'   id = letters[1:3],
#'   col_var = letters[1:3]
#' )
#'
#' adsl_f <- tern_ex_adsl %>%
#'   select(USUBJID, STUDYID, ARM, ARMCD, SEX)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "OVRINV") %>%
#'   mutate(pchg = rnorm(n(), 10, 50))
#'
#' adrs_f <- head(adrs_f, 30)
#' adrs_f <- adrs_f[!duplicated(adrs_f$USUBJID), ]
#' head(adrs_f)
#'
#' g_waterfall(
#'   height = adrs_f$pchg,
#'   id = adrs_f$USUBJID,
#'   col_var = adrs_f$AVALC
#' )
#'
#' g_waterfall(
#'   height = adrs_f$pchg,
#'   id = paste("asdfdsfdsfsd", adrs_f$USUBJID),
#'   col_var = adrs_f$SEX
#' )
#'
#' g_waterfall(
#'   height = adrs_f$pchg,
#'   id = paste("asdfdsfdsfsd", adrs_f$USUBJID),
#'   xlab = "ID",
#'   ylab = "Percentage Change",
#'   title = "Waterfall plot"
#' )
#'
#' @export
g_waterfall <- function(height,
                        id,
                        col_var = NULL,
                        col = getOption("ggplot2.discrete.colour"),
                        xlab = NULL,
                        ylab = NULL,
                        col_legend_title = NULL,
                        title = NULL) {
  if (!is.null(col_var)) {
    check_same_n(height = height, id = id, col_var = col_var)
  } else {
    check_same_n(height = height, id = id)
  }

  checkmate::assert_multi_class(col_var, c("character", "factor"), null.ok = TRUE)
  checkmate::assert_character(col, null.ok = TRUE)

  xlabel <- deparse(substitute(id))
  ylabel <- deparse(substitute(height))

  col_label <- if (!missing(col_var)) {
    deparse(substitute(col_var))
  }

  xlab <- if (is.null(xlab)) xlabel else xlab
  ylab <- if (is.null(ylab)) ylabel else ylab
  col_legend_title <- if (is.null(col_legend_title)) col_label else col_legend_title

  plot_data <- data.frame(
    height = height,
    id = as.character(id),
    col_var = if (is.null(col_var)) "x" else to_n(col_var, length(height)),
    stringsAsFactors = FALSE
  )

  plot_data_ord <- plot_data[order(plot_data$height, decreasing = TRUE), ]

  p <- ggplot2::ggplot(plot_data_ord, ggplot2::aes(x = factor(id, levels = id), y = height)) +
    ggplot2::geom_col() +
    ggplot2::geom_text(
      label = format(plot_data_ord$height, digits = 2),
      vjust = ifelse(plot_data_ord$height >= 0, -0.5, 1.5)
    ) +
    ggplot2::xlab(xlab) +
    ggplot2::ylab(ylab) +
    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 0, vjust = .5))

  if (!is.null(col_var)) {
    p <- p +
      ggplot2::aes(fill = col_var) +
      ggplot2::labs(fill = col_legend_title) +
      ggplot2::theme(
        legend.position = "bottom",
        legend.background = ggplot2::element_blank(),
        legend.title = ggplot2::element_text(face = "bold"),
        legend.box.background = ggplot2::element_rect(colour = "black")
      )
  }

  if (!is.null(col)) {
    p <- p +
      ggplot2::scale_fill_manual(values = col)
  }

  if (!is.null(title)) {
    p <- p +
      ggplot2::labs(title = title) +
      ggplot2::theme(plot.title = ggplot2::element_text(face = "bold"))
  }

  p
}

#' Helper Functions for Tabulating Biomarker Effects on Binary Response by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions which are documented here separately to not confuse the user
#' when reading about the user-facing functions.
#'
#' @inheritParams response_biomarkers_subgroups
#' @inheritParams extract_rsp_biomarkers
#' @inheritParams argument_convention
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   mutate(rsp = AVALC == "CR")
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' @name h_response_biomarkers_subgroups
NULL

#' @describeIn h_response_biomarkers_subgroups helps with converting the "response" function variable list
#'   to the "logistic regression" variable list. The reason is that currently there is an
#'   inconsistency between the variable names accepted by `extract_rsp_subgroups()` and `fit_logistic()`.
#'
#' @param biomarker (`string`)\cr the name of the biomarker variable.
#'
#' @return
#' * `h_rsp_to_logistic_variables()` returns a named `list` of elements `response`, `arm`, `covariates`, and `strata`.
#'
#' @examples
#' # This is how the variable list is converted internally.
#' h_rsp_to_logistic_variables(
#'   variables = list(
#'     rsp = "RSP",
#'     covariates = c("A", "B"),
#'     strata = "D"
#'   ),
#'   biomarker = "AGE"
#' )
#'
#' @export
h_rsp_to_logistic_variables <- function(variables, biomarker) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_rsp_to_logistic_variables() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }
  checkmate::assert_list(variables)
  checkmate::assert_string(variables$rsp)
  checkmate::assert_string(biomarker)
  list(
    response = variables$rsp,
    arm = biomarker,
    covariates = variables$covariates,
    strata = variables$strata
  )
}

#' @describeIn h_response_biomarkers_subgroups prepares estimates for number of responses, patients and
#'   overall response rate, as well as odds ratio estimates, confidence intervals and p-values, for multiple
#'   biomarkers in a given single data set.
#'   `variables` corresponds to names of variables found in `data`, passed as a named list and requires elements
#'   `rsp` and `biomarkers` (vector of continuous biomarker variables) and optionally `covariates`
#'   and `strata`.
#'
#' @return
#' * `h_logistic_mult_cont_df()` returns a `data.frame` containing estimates and statistics for the selected biomarkers.
#'
#' @examples
#' # For a single population, estimate separately the effects
#' # of two biomarkers.
#' df <- h_logistic_mult_cont_df(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX"
#'   ),
#'   data = adrs_f
#' )
#' df
#'
#' # If the data set is empty, still the corresponding rows with missings are returned.
#' h_coxreg_mult_cont_df(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     strata = "STRATA1"
#'   ),
#'   data = adrs_f[NULL, ]
#' )
#'
#' @export
h_logistic_mult_cont_df <- function(variables,
                                    data,
                                    control = control_logistic()) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_logistic_mult_cont_df() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }
  assert_df_with_variables(data, variables)

  checkmate::assert_character(variables$biomarkers, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")

  conf_level <- control[["conf_level"]]
  pval_label <- "p-value (Wald)"

  # If there is any data, run model, otherwise return empty results.
  if (nrow(data) > 0) {
    bm_cols <- match(variables$biomarkers, names(data))
    l_result <- lapply(variables$biomarkers, function(bm) {
      model_fit <- fit_logistic(
        variables = h_rsp_to_logistic_variables(variables, bm),
        data = data,
        response_definition = control$response_definition
      )
      result <- h_logistic_simple_terms(
        x = bm,
        fit_glm = model_fit,
        conf_level = control$conf_level
      )
      resp_vector <- if (inherits(model_fit, "glm")) {
        model_fit$model[[variables$rsp]]
      } else {
        as.logical(as.matrix(model_fit$y)[, "status"])
      }
      data.frame(
        # Dummy column needed downstream to create a nested header.
        biomarker = bm,
        biomarker_label = formatters::var_labels(data[bm], fill = TRUE),
        n_tot = length(resp_vector),
        n_rsp = sum(resp_vector),
        prop = mean(resp_vector),
        or = as.numeric(result[1L, "odds_ratio"]),
        lcl = as.numeric(result[1L, "lcl"]),
        ucl = as.numeric(result[1L, "ucl"]),
        conf_level = conf_level,
        pval = as.numeric(result[1L, "pvalue"]),
        pval_label = pval_label,
        stringsAsFactors = FALSE
      )
    })
    do.call(rbind, args = c(l_result, make.row.names = FALSE))
  } else {
    data.frame(
      biomarker = variables$biomarkers,
      biomarker_label = formatters::var_labels(data[variables$biomarkers], fill = TRUE),
      n_tot = 0L,
      n_rsp = 0L,
      prop = NA,
      or = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      pval = NA,
      pval_label = pval_label,
      row.names = seq_along(variables$biomarkers),
      stringsAsFactors = FALSE
    )
  }
}

#' @describeIn h_response_biomarkers_subgroups prepares a single sub-table given a `df_sub` containing
#'   the results for a single biomarker.
#'
#' @param df (`data.frame`)\cr results for a single biomarker, as part of what is
#'   returned by [extract_rsp_biomarkers()] (it needs a couple of columns which are
#'   added by that high-level function relative to what is returned by [h_logistic_mult_cont_df()],
#'   see the example).
#'
#' @return
#' * `h_tab_rsp_one_biomarker()` returns an `rtables` table object with the given statistics arranged in columns.
#'
#' @examples
#' # Starting from above `df`, zoom in on one biomarker and add required columns.
#' df1 <- df[1, ]
#' df1$subgroup <- "All patients"
#' df1$row_type <- "content"
#' df1$var <- "ALL"
#' df1$var_label <- "All patients"
#'
#' h_tab_rsp_one_biomarker(
#'   df1,
#'   vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval")
#' )
#'
#' @export
h_tab_rsp_one_biomarker <- function(df,
                                    vars,
                                    na_str = default_na_str(),
                                    .indent_mods = 0L) {
  afuns <- a_response_subgroups(na_str = na_str)[vars]
  colvars <- d_rsp_subgroups_colvars(
    vars,
    conf_level = df$conf_level[1],
    method = df$pval_label[1]
  )
  h_tab_one_biomarker(
    df = df,
    afuns = afuns,
    colvars = colvars,
    na_str = na_str,
    .indent_mods = .indent_mods
  )
}

#' Individual Patient Plots
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Line plot(s) displaying trend in patients' parameter values over time is rendered.
#' Patients' individual baseline values can be added to the plot(s) as reference.
#'
#' @inheritParams argument_convention
#' @param xvar (`string`)\cr time point variable to be plotted on x-axis.
#' @param yvar (`string`)\cr continuous analysis variable to be plotted on y-axis.
#' @param xlab (`string`)\cr plot label for x-axis.
#' @param ylab (`string`)\cr plot label for y-axis.
#' @param id_var (`string`)\cr variable used as patient identifier.
#' @param title (`string`)\cr title for plot.
#' @param subtitle (`string`)\cr subtitle for plot.
#' @param add_baseline_hline (`flag`)\cr adds horizontal line at baseline y-value on
#'   plot when TRUE.
#' @param yvar_baseline (`string`)\cr variable with baseline values only.
#'   Ignored when `add_baseline_hline` is FALSE.
#' @param ggtheme (`theme`)\cr optional graphical theme function as provided
#'   by `ggplot2` to control outlook of plot. Use `ggplot2::theme()` to tweak the display.
#' @param plotting_choices (`character`)\cr specifies options for displaying
#'   plots. Must be one of "all_in_one", "split_by_max_obs", "separate_by_obs".
#' @param max_obs_per_plot (`count`)\cr Number of observations to be plotted on one
#'   plot. Ignored when `plotting_choices` is not "separate_by_obs".
#' @param caption (`character` scalar)\cr optional caption below the plot.
#' @param col (`character`)\cr lines colors.
#'
#' @seealso Relevant helper function [h_g_ipp()].
#'
#' @name individual_patient_plot
NULL

#' Helper Function To Create Simple Line Plot over Time
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Function that generates a simple line plot displaying parameter trends over time.
#'
#' @inheritParams argument_convention
#' @inheritParams g_ipp
#'
#' @return A `ggplot` line plot.
#'
#' @seealso [g_ipp()] which uses this function.
#'
#' @examples
#' library(dplyr)
#' library(nestcolor)
#'
#' # Select a small sample of data to plot.
#' adlb <- tern_ex_adlb %>%
#'   filter(PARAMCD == "ALT", !(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
#'   slice(1:36)
#'
#' p <- h_g_ipp(
#'   df = adlb,
#'   xvar = "AVISIT",
#'   yvar = "AVAL",
#'   xlab = "Visit",
#'   id_var = "USUBJID",
#'   ylab = "SGOT/ALT (U/L)",
#'   add_baseline_hline = TRUE
#' )
#' p
#'
#' @export
h_g_ipp <- function(df,
                    xvar,
                    yvar,
                    xlab,
                    ylab,
                    id_var,
                    title = "Individual Patient Plots",
                    subtitle = "",
                    caption = NULL,
                    add_baseline_hline = FALSE,
                    yvar_baseline = "BASE",
                    ggtheme = nestcolor::theme_nest(),
                    col = NULL) {
  checkmate::assert_string(xvar)
  checkmate::assert_string(yvar)
  checkmate::assert_string(yvar_baseline)
  checkmate::assert_string(id_var)
  checkmate::assert_string(xlab)
  checkmate::assert_string(ylab)
  checkmate::assert_string(title)
  checkmate::assert_string(subtitle)
  checkmate::assert_subset(c(xvar, yvar, yvar_baseline, id_var), colnames(df))
  checkmate::assert_data_frame(df)
  checkmate::assert_flag(add_baseline_hline)
  checkmate::assert_character(col, null.ok = TRUE)

  p <- ggplot2::ggplot(
    data = df,
    mapping = ggplot2::aes(
      x = .data[[xvar]],
      y = .data[[yvar]],
      group = .data[[id_var]],
      colour = .data[[id_var]]
    )
  ) +
    ggplot2::geom_line(linewidth = 0.4) +
    ggplot2::geom_point(size = 2) +
    ggplot2::labs(
      x = xlab,
      y = ylab,
      title = title,
      subtitle = subtitle,
      caption = caption
    ) +
    ggtheme

  if (add_baseline_hline) {
    baseline_df <- df[, c(id_var, yvar_baseline)]
    baseline_df <- unique(baseline_df)

    p <- p +
      ggplot2::geom_hline(
        data = baseline_df,
        mapping = ggplot2::aes(
          yintercept = .data[[yvar_baseline]],
          colour = .data[[id_var]]
        ),
        linetype = "dotdash",
        linewidth = 0.4
      ) +
      ggplot2::geom_text(
        data = baseline_df,
        mapping = ggplot2::aes(
          x = 1,
          y = .data[[yvar_baseline]],
          label = .data[[id_var]],
          colour = .data[[id_var]]
        ),
        nudge_y = 0.025 * (max(df[, yvar], na.rm = TRUE) - min(df[, yvar], na.rm = TRUE)),
        vjust = "right",
        size = 2
      )

    if (!is.null(col)) {
      p <- p +
        ggplot2::scale_color_manual(values = col)
    }
  }
  p
}

#' @describeIn individual_patient_plot Plotting function for individual patient plots which, depending on user
#'   preference, renders a single graphic or compiles a list of graphics that show trends in individual's parameter
#'   values over time.
#'
#' @return A `ggplot` object or a list of `ggplot` objects.
#'
#' @examples
#' library(dplyr)
#' library(nestcolor)
#'
#' # Select a small sample of data to plot.
#' adlb <- tern_ex_adlb %>%
#'   filter(PARAMCD == "ALT", !(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
#'   slice(1:36)
#'
#' plot_list <- g_ipp(
#'   df = adlb,
#'   xvar = "AVISIT",
#'   yvar = "AVAL",
#'   xlab = "Visit",
#'   ylab = "SGOT/ALT (U/L)",
#'   title = "Individual Patient Plots",
#'   add_baseline_hline = TRUE,
#'   plotting_choices = "split_by_max_obs",
#'   max_obs_per_plot = 5
#' )
#' plot_list
#'
#' @export
g_ipp <- function(df,
                  xvar,
                  yvar,
                  xlab,
                  ylab,
                  id_var = "USUBJID",
                  title = "Individual Patient Plots",
                  subtitle = "",
                  caption = NULL,
                  add_baseline_hline = FALSE,
                  yvar_baseline = "BASE",
                  ggtheme = nestcolor::theme_nest(),
                  plotting_choices = c("all_in_one", "split_by_max_obs", "separate_by_obs"),
                  max_obs_per_plot = 4,
                  col = NULL) {
  checkmate::assert_count(max_obs_per_plot)
  checkmate::assert_subset(plotting_choices, c("all_in_one", "split_by_max_obs", "separate_by_obs"))
  checkmate::assert_character(col, null.ok = TRUE)

  plotting_choices <- match.arg(plotting_choices)

  if (plotting_choices == "all_in_one") {
    p <- h_g_ipp(
      df = df,
      xvar = xvar,
      yvar = yvar,
      xlab = xlab,
      ylab = ylab,
      id_var = id_var,
      title = title,
      subtitle = subtitle,
      caption = caption,
      add_baseline_hline = add_baseline_hline,
      yvar_baseline = yvar_baseline,
      ggtheme = ggtheme,
      col = col
    )

    return(p)
  } else if (plotting_choices == "split_by_max_obs") {
    id_vec <- unique(df[[id_var]])
    id_list <- split(
      id_vec,
      rep(1:ceiling(length(id_vec) / max_obs_per_plot),
        each = max_obs_per_plot,
        length.out = length(id_vec)
      )
    )

    df_list <- list()
    plot_list <- list()

    for (i in seq_along(id_list)) {
      df_list[[i]] <- df[df[[id_var]] %in% id_list[[i]], ]

      plots <- h_g_ipp(
        df = df_list[[i]],
        xvar = xvar,
        yvar = yvar,
        xlab = xlab,
        ylab = ylab,
        id_var = id_var,
        title = title,
        subtitle = subtitle,
        caption = caption,
        add_baseline_hline = add_baseline_hline,
        yvar_baseline = yvar_baseline,
        ggtheme = ggtheme,
        col = col
      )

      plot_list[[i]] <- plots
    }
    return(plot_list)
  } else {
    ind_df <- split(df, df[[id_var]])
    plot_list <- lapply(
      ind_df,
      function(x) {
        h_g_ipp(
          df = x,
          xvar = xvar,
          yvar = yvar,
          xlab = xlab,
          ylab = ylab,
          id_var = id_var,
          title = title,
          subtitle = subtitle,
          caption = caption,
          add_baseline_hline = add_baseline_hline,
          yvar_baseline = yvar_baseline,
          ggtheme = ggtheme,
          col = col
        )
      }
    )

    return(plot_list)
  }
}

#' Univariate Formula Special Term
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The special term `univariate` indicate that the model should be fitted individually for
#' every variable included in univariate.
#'
#' @param x A vector of variable name separated by commas.
#'
#' @return When used within a model formula, produces univariate models for each variable provided.
#'
#' @details
#' If provided alongside with pairwise specification, the model
#' `y ~ ARM + univariate(SEX, AGE, RACE)` lead to the study and comparison of the models
#' + `y ~ ARM`
#' + `y ~ ARM + SEX`
#' + `y ~ ARM + AGE`
#' + `y ~ ARM + RACE`
#'
#' @export
univariate <- function(x) {
  structure(x, varname = deparse(substitute(x)))
}

# Get the right-hand-term of a formula
rht <- function(x) {
  checkmate::assert_formula(x)
  y <- as.character(rev(x)[[1]])
  return(y)
}

#' Hazard Ratio Estimation in Interactions
#'
#' This function estimates the hazard ratios between arms when an interaction variable is given with
#' specific values.
#'
#' @param variable,given Names of two variable in interaction. We seek the estimation of the levels of `variable`
#'   given the levels of `given`.
#' @param lvl_var,lvl_given corresponding levels has given by `levels`.
#' @param mmat A name numeric filled with 0 used as template to obtain the design matrix.
#' @param coef Numeric of estimated coefficients.
#' @param vcov Variance-covariance matrix of underlying model.
#' @param conf_level Single numeric for the confidence level of estimate intervals.
#'
#' @details Given the cox regression investigating the effect of Arm (A, B, C; reference A)
#'   and Sex (F, M; reference Female). The model is abbreviated: y ~ Arm + Sex + Arm x Sex.
#'   The cox regression estimates the coefficients along with a variance-covariance matrix for:
#'
#'   - b1 (arm b), b2 (arm c)
#'   - b3 (sex m)
#'   - b4 (arm b: sex m), b5 (arm c: sex m)
#'
#'   Given that I want an estimation of the Hazard Ratio for arm C/sex M, the estimation
#'   will be given in reference to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5),
#'   therefore the interaction coefficient is given by b2 + b5 while the standard error is obtained
#'   as $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ for a confidence level of 0.95.
#'
#' @return A list of matrix (one per level of variable) with rows corresponding to the combinations of
#'   `variable` and `given`, with columns:
#'   * `coef_hat`: Estimation of the coefficient.
#'   * `coef_se`: Standard error of the estimation.
#'   * `hr`: Hazard ratio.
#'   * `lcl, ucl`: Lower/upper confidence limit of the hazard ratio.
#'
#' @seealso [s_cox_multivariate()].
#'
#' @examples
#' library(dplyr)
#' library(survival)
#'
#' ADSL <- tern_ex_adsl %>%
#'   filter(SEX %in% c("F", "M"))
#'
#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "PFS")
#' adtte$ARMCD <- droplevels(adtte$ARMCD)
#' adtte$SEX <- droplevels(adtte$SEX)
#'
#' mod <- coxph(
#'   formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2,
#'   data = adtte
#' )
#'
#' mmat <- stats::model.matrix(mod)[1, ]
#' mmat[!mmat == 0] <- 0
#'
#' @keywords internal
estimate_coef <- function(variable, given,
                          lvl_var, lvl_given,
                          coef,
                          mmat,
                          vcov,
                          conf_level = 0.95) {
  var_lvl <- paste0(variable, lvl_var[-1]) # [-1]: reference level
  giv_lvl <- paste0(given, lvl_given)

  design_mat <- expand.grid(variable = var_lvl, given = giv_lvl)
  design_mat <- design_mat[order(design_mat$variable, design_mat$given), ]
  design_mat <- within(
    data = design_mat,
    expr = {
      inter <- paste0(variable, ":", given)
      rev_inter <- paste0(given, ":", variable)
    }
  )

  split_by_variable <- design_mat$variable
  interaction_names <- paste(design_mat$variable, design_mat$given, sep = "/")

  design_mat <- apply(
    X = design_mat, MARGIN = 1, FUN = function(x) {
      mmat[names(mmat) %in% x[-which(names(x) == "given")]] <- 1
      return(mmat)
    }
  )
  colnames(design_mat) <- interaction_names

  betas <- as.matrix(coef)

  coef_hat <- t(design_mat) %*% betas
  dimnames(coef_hat)[2] <- "coef"

  coef_se <- apply(design_mat, 2, function(x) {
    vcov_el <- as.logical(x)
    y <- vcov[vcov_el, vcov_el]
    y <- sum(y)
    y <- sqrt(y)
    return(y)
  })

  q_norm <- stats::qnorm((1 + conf_level) / 2)
  y <- cbind(coef_hat, `se(coef)` = coef_se)

  y <- apply(y, 1, function(x) {
    x["hr"] <- exp(x["coef"])
    x["lcl"] <- exp(x["coef"] - q_norm * x["se(coef)"])
    x["ucl"] <- exp(x["coef"] + q_norm * x["se(coef)"])

    return(x)
  })

  y <- t(y)
  y <- by(y, split_by_variable, identity)
  y <- lapply(y, as.matrix)

  attr(y, "details") <- paste0(
    "Estimations of ", variable,
    " hazard ratio given the level of ", given, " compared to ",
    variable, " level ", lvl_var[1], "."
  )
  return(y)
}

#' `tryCatch` around `car::Anova`
#'
#' Captures warnings when executing [car::Anova].
#'
#' @inheritParams car::Anova
#'
#' @return A list with item `aov` for the result of the model and `error_text` for the captured warnings.
#'
#' @examples
#' # `car::Anova` on cox regression model including strata and expected
#' # a likelihood ratio test triggers a warning as only `Wald` method is
#' # accepted.
#'
#' library(survival)
#'
#' mod <- coxph(
#'   formula = Surv(time = futime, event = fustat) ~ factor(rx) + strata(ecog.ps),
#'   data = ovarian
#' )
#'
#' @keywords internal
try_car_anova <- function(mod,
                          test.statistic) { # nolint
  y <- tryCatch(
    withCallingHandlers(
      expr = {
        warn_text <- c()
        list(
          aov = car::Anova(
            mod,
            test.statistic = test.statistic,
            type = "III"
          ),
          warn_text = warn_text
        )
      },
      warning = function(w) {
        # If a warning is detected it is handled as "w".
        warn_text <<- trimws(paste0("Warning in `try_car_anova`: ", w))

        # A warning is sometimes expected, then, we want to restart
        # the execution while ignoring the warning.
        invokeRestart("muffleWarning")
      }
    ),
    finally = {
    }
  )

  return(y)
}

#' Fit the Cox Regression Model and `Anova`
#'
#' The functions allows to derive from the [survival::coxph()] results the effect p.values using [car::Anova()].
#' This last package introduces more flexibility to get the effect p.values.
#'
#' @inheritParams t_coxreg
#'
#' @return A list with items `mod` (results of [survival::coxph()]), `msum` (result of `summary`) and
#'   `aov` (result of [car::Anova()]).
#'
#' @noRd
fit_n_aov <- function(formula,
                      data = data,
                      conf_level = conf_level,
                      pval_method = c("wald", "likelihood"),
                      ...) {
  pval_method <- match.arg(pval_method)

  environment(formula) <- environment()
  suppressWarnings({
    # We expect some warnings due to coxph which fails strict programming.
    mod <- survival::coxph(formula, data = data, ...)
    msum <- summary(mod, conf.int = conf_level)
  })

  aov <- try_car_anova(
    mod,
    test.statistic = switch(pval_method,
      "wald" = "Wald",
      "likelihood" = "LR"
    )
  )

  warn_attr <- aov$warn_text
  if (!is.null(aov$warn_text)) message(warn_attr)

  aov <- aov$aov
  y <- list(mod = mod, msum = msum, aov = aov)
  attr(y, "message") <- warn_attr

  return(y)
}

# argument_checks
check_formula <- function(formula) {
  if (!(inherits(formula, "formula"))) {
    stop("Check `formula`. A formula should resemble `Surv(time = AVAL, event = 1 - CNSR) ~ study_arm(ARMCD)`.")
  }

  invisible()
}

check_covariate_formulas <- function(covariates) {
  if (!all(vapply(X = covariates, FUN = inherits, what = "formula", FUN.VALUE = TRUE)) || is.null(covariates)) {
    stop("Check `covariates`, it should be a list of right-hand-term formulas, e.g. list(Age = ~AGE).")
  }

  invisible()
}

name_covariate_names <- function(covariates) {
  miss_names <- names(covariates) == ""
  no_names <- is.null(names(covariates))
  if (any(miss_names)) names(covariates)[miss_names] <- vapply(covariates[miss_names], FUN = rht, FUN.VALUE = "name")
  if (no_names) names(covariates) <- vapply(covariates, FUN = rht, FUN.VALUE = "name")
  return(covariates)
}

check_increments <- function(increments, covariates) {
  if (!is.null(increments)) {
    covariates <- vapply(covariates, FUN = rht, FUN.VALUE = "name")
    lapply(
      X = names(increments), FUN = function(x) {
        if (!x %in% covariates) {
          warning(
            paste(
              "Check `increments`, the `increment` for ", x,
              "doesn't match any names in investigated covariate(s)."
            )
          )
        }
      }
    )
  }

  invisible()
}

#' Multivariate Cox Model - Summarized Results
#'
#' Analyses based on multivariate Cox model are usually not performed for the Controlled Substance Reporting or
#' regulatory documents but serve exploratory purposes only (e.g., for publication). In practice, the model usually
#' includes only the main effects (without interaction terms). It produces the hazard ratio estimates for each of the
#' covariates included in the model.
#' The analysis follows the same principles (e.g., stratified vs. unstratified analysis and tie handling) as the
#' usual Cox model analysis. Since there is usually no pre-specified hypothesis testing for such analysis,
#' the p.values need to be interpreted with caution. (**Statistical Analysis of Clinical Trials Data with R**,
#' `NEST's bookdown`)
#'
#' @param formula (`formula`)\cr A formula corresponding to the investigated [survival::Surv()] survival model
#'   including covariates.
#' @param data (`data.frame`)\cr A data frame which includes the variable in formula and covariates.
#' @param conf_level (`proportion`)\cr The confidence level for the hazard ratio interval estimations. Default is 0.95.
#' @param pval_method (`character`)\cr The method used for the estimation of p-values, should be one of
#'   `"wald"` (default) or `"likelihood"`.
#' @param ... Optional parameters passed to [survival::coxph()]. Can include `ties`, a character string specifying the
#'   method for tie handling, one of `exact` (default), `efron`, `breslow`.
#'
#' @return A `list` with elements `mod`, `msum`, `aov`, and `coef_inter`.
#'
#' @details The output is limited to single effect terms. Work in ongoing for estimation of interaction terms
#'   but is out of scope as defined by the  Global Data Standards Repository
#'   (**`GDS_Standard_TLG_Specs_Tables_2.doc`**).
#'
#' @seealso [estimate_coef()].
#'
#' @examples
#' library(dplyr)
#'
#' adtte <- tern_ex_adtte
#' adtte_f <- subset(adtte, PARAMCD == "OS") # _f: filtered
#' adtte_f <- filter(
#'   adtte_f,
#'   PARAMCD == "OS" &
#'     SEX %in% c("F", "M") &
#'     RACE %in% c("ASIAN", "BLACK OR AFRICAN AMERICAN", "WHITE")
#' )
#' adtte_f$SEX <- droplevels(adtte_f$SEX)
#' adtte_f$RACE <- droplevels(adtte_f$RACE)
#'
#' @keywords internal
s_cox_multivariate <- function(formula, data,
                               conf_level = 0.95,
                               pval_method = c("wald", "likelihood"),
                               ...) {
  tf <- stats::terms(formula, specials = c("strata"))
  covariates <- rownames(attr(tf, "factors"))[-c(1, unlist(attr(tf, "specials")))]
  lapply(
    X = covariates,
    FUN = function(x) {
      if (is.character(data[[x]])) {
        data[[x]] <<- as.factor(data[[x]])
      }
      invisible()
    }
  )
  pval_method <- match.arg(pval_method)

  # Results directly exported from environment(fit_n_aov) to environment(s_function_draft)
  y <- fit_n_aov(
    formula = formula,
    data = data,
    conf_level = conf_level,
    pval_method = pval_method,
    ...
  )
  mod <- y$mod
  aov <- y$aov
  msum <- y$msum
  list2env(as.list(y), environment())

  all_term_labs <- attr(mod$terms, "term.labels")
  term_labs <- all_term_labs[which(attr(mod$terms, "order") == 1)]
  names(term_labs) <- term_labs

  coef_inter <- NULL
  if (any(attr(mod$terms, "order") > 1)) {
    for_inter <- all_term_labs[attr(mod$terms, "order") > 1]
    names(for_inter) <- for_inter
    mmat <- stats::model.matrix(mod)[1, ]
    mmat[!mmat == 0] <- 0
    mcoef <- stats::coef(mod)
    mvcov <- stats::vcov(mod)

    estimate_coef_local <- function(variable, given) {
      estimate_coef(
        variable, given,
        coef = mcoef, mmat = mmat, vcov = mvcov, conf_level = conf_level,
        lvl_var = levels(data[[variable]]), lvl_given = levels(data[[given]])
      )
    }

    coef_inter <- lapply(
      for_inter, function(x) {
        y <- attr(mod$terms, "factors")[, x]
        y <- names(y[y > 0])
        Map(estimate_coef_local, variable = y, given = rev(y))
      }
    )
  }

  list(mod = mod, msum = msum, aov = aov, coef_inter = coef_inter)
}

#' Tabulate Biomarker Effects on Survival by Subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Tabulate the estimated effects of multiple continuous biomarker variables
#' across population subgroups.
#'
#' @inheritParams fit_coxreg_multivar
#' @inheritParams survival_duration_subgroups
#' @inheritParams argument_convention
#' @param df (`data.frame`)\cr containing all analysis variables, as returned by
#'   [extract_survival_biomarkers()].
#' @param vars (`character`)\cr the names of statistics to be reported among:
#'   * `n_tot_events`: Total number of events per group.
#'   * `n_tot`: Total number of observations per group.
#'   * `median`: Median survival time.
#'   * `hr`: Hazard ratio.
#'   * `ci`: Confidence interval of hazard ratio.
#'   * `pval`: p-value of the effect.
#'   Note, one of the statistics `n_tot` and `n_tot_events`, as well as both `hr` and `ci` are required.
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. The tables are then typically used as input for forest plots.
#'
#' @examples
#' library(dplyr)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte)
#'
#' adtte_f <- adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
#' # in multiple regression models containing one covariate `RACE`,
#' # as well as one stratification variable `STRATA1`. The subgroups
#' # are defined by the levels of `BMRKR2`.
#'
#' df <- extract_survival_biomarkers(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     strata = "STRATA1",
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   data = adtte_f
#' )
#' df
#'
#' # Here we group the levels of `BMRKR2` manually.
#' df_grouped <- extract_survival_biomarkers(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     strata = "STRATA1",
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#' df_grouped
#'
#' @name survival_biomarkers_subgroups
#' @order 1
NULL

#' Prepares Survival Data Estimates for Multiple Biomarkers in a Single Data Frame
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares estimates for number of events, patients and median survival times, as well as hazard ratio estimates,
#' confidence intervals and p-values, for multiple biomarkers across population subgroups in a single data frame.
#' `variables` corresponds to the names of variables found in `data`, passed as a named `list` and requires elements
#' `tte`, `is_event`, `biomarkers` (vector of continuous biomarker variables), and optionally `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams fit_coxreg_multivar
#' @inheritParams survival_duration_subgroups
#'
#' @return A `data.frame` with columns `biomarker`, `biomarker_label`, `n_tot`, `n_tot_events`,
#'   `median`, `hr`, `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`,
#'   `var_label`, and `row_type`.
#'
#' @seealso [h_coxreg_mult_cont_df()] which is used internally, [tabulate_survival_biomarkers()].
#'
#' @export
extract_survival_biomarkers <- function(variables,
                                        data,
                                        groups_lists = list(),
                                        control = control_coxreg(),
                                        label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_survival_biomarkers() ",
      "was deprecated in tern 0.9.3.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  checkmate::assert_list(variables)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_string(label_all)

  # Start with all patients.
  result_all <- h_coxreg_mult_cont_df(
    variables = variables,
    data = data,
    control = control
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"
  if (is.null(variables$subgroups)) {
    # Only return result for all patients.
    result_all
  } else {
    # Add subgroups results.
    l_data <- h_split_by_subgroups(
      data,
      variables$subgroups,
      groups_lists = groups_lists
    )
    l_result <- lapply(l_data, function(grp) {
      result <- h_coxreg_mult_cont_df(
        variables = variables,
        data = grp$df,
        control = control
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"
    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' @describeIn survival_biomarkers_subgroups Table-creating function which creates a table
#'   summarizing biomarker effects on survival by subgroup.
#'
#' @return An `rtables` table summarizing biomarker effects on survival by subgroup.
#'
#' @note In contrast to [tabulate_survival_subgroups()] this tabulation function does
#'   not start from an input layout `lyt`. This is because internally the table is
#'   created by combining multiple subtables.
#'
#' @seealso [h_tab_surv_one_biomarker()] which is used internally, [extract_survival_biomarkers()].
#'
#' @examples
#' ## Table with default columns.
#' tabulate_survival_biomarkers(df)
#'
#' ## Table with a manually chosen set of columns: leave out "pval", reorder.
#' tab <- tabulate_survival_biomarkers(
#'   df = df,
#'   vars = c("n_tot_events", "ci", "n_tot", "median", "hr"),
#'   time_unit = as.character(adtte_f$AVALU[1])
#' )
#'
#' ## Finally produce the forest plot.
#' \donttest{
#' g_forest(tab, xlim = c(0.8, 1.2))
#' }
#'
#' @export
#' @order 2
tabulate_survival_biomarkers <- function(df,
                                         vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
                                         groups_lists = list(),
                                         control = control_coxreg(),
                                         label_all = "All Patients",
                                         time_unit = NULL,
                                         na_str = default_na_str(),
                                         .indent_mods = 0L) {
  checkmate::assert_data_frame(df)
  checkmate::assert_character(df$biomarker)
  checkmate::assert_character(df$biomarker_label)
  checkmate::assert_subset(vars, get_stats("tabulate_survival_biomarkers"))

  extra_args <- list(groups_lists = groups_lists, control = control, label_all = label_all)

  df_subs <- split(df, f = df$biomarker)
  tabs <- lapply(df_subs, FUN = function(df_sub) {
    tab_sub <- h_tab_surv_one_biomarker(
      df = df_sub,
      vars = vars,
      time_unit = time_unit,
      na_str = na_str,
      .indent_mods = .indent_mods,
      extra_args = extra_args
    )
    # Insert label row as first row in table.
    label_at_path(tab_sub, path = row_paths(tab_sub)[[1]][1]) <- df_sub$biomarker_label[1]
    tab_sub
  })
  result <- do.call(rbind, tabs)

  n_tot_ids <- grep("^n_tot", vars)
  hr_id <- match("hr", vars)
  ci_id <- match("ci", vars)
  structure(
    result,
    forest_header = paste0(c("Higher", "Lower"), "\nBetter"),
    col_x = hr_id,
    col_ci = ci_id,
    col_symbol_size = n_tot_ids[1]
  )
}

#' Patient Counts with the Most Extreme Post-baseline Toxicity Grade per Direction of Abnormality
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Primary analysis variable `.var` indicates the toxicity grade (`factor`), and additional
#' analysis variables are `id` (`character` or `factor`), `param` (`factor`) and `grade_dir` (`factor`).
#' The pre-processing steps are crucial when using this function.
#' For a certain direction (e.g. high or low) this function counts
#' patients in the denominator as number of patients with at least one valid measurement during treatment,
#' and patients in the numerator as follows:
#'   * `1` to `4`: Numerator is number of patients with worst grades 1-4 respectively;
#'   * `Any`: Numerator is number of patients with at least one abnormality, which means grade is different from 0.
#'
#' Pre-processing is crucial when using this function and can be done automatically using the
#' [h_adlb_abnormal_by_worst_grade()] helper function. See the description of this function for details on the
#' necessary pre-processing steps.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("abnormal_by_worst_grade")`
#'   to see available statistics for this function.
#'
#' @seealso [h_adlb_abnormal_by_worst_grade()] which pre-processes `ADLB` data frames to be used in
#'   [count_abnormal_by_worst_grade()].
#'
#' @name abnormal_by_worst_grade
#' @order 1
NULL

#' @describeIn abnormal_by_worst_grade Statistics function which counts patients by worst grade.
#'
#' @return
#' * `s_count_abnormal_by_worst_grade()` returns the single statistic `count_fraction` with grades 1 to 4 and
#'   "Any" results.
#'
#' @keywords internal
s_count_abnormal_by_worst_grade <- function(df, # nolint
                                            .var = "GRADE_ANL",
                                            .spl_context,
                                            variables = list(
                                              id = "USUBJID",
                                              param = "PARAM",
                                              grade_dir = "GRADE_DIR"
                                            )) {
  checkmate::assert_string(.var)
  assert_valid_factor(df[[.var]])
  assert_valid_factor(df[[variables$param]])
  assert_valid_factor(df[[variables$grade_dir]])
  assert_df_with_variables(df, c(a = .var, variables))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))

  # To verify that the `split_rows_by` are performed with correct variables.
  checkmate::assert_subset(c(variables[["param"]], variables[["grade_dir"]]), .spl_context$split)
  first_row <- .spl_context[.spl_context$split == variables[["param"]], ]
  x_lvls <- c(setdiff(levels(df[[.var]]), "0"), "Any")
  result <- split(numeric(0), factor(x_lvls))

  subj <- first_row$full_parent_df[[1]][[variables[["id"]]]]
  subj_cur_col <- subj[first_row$cur_col_subset[[1]]]
  # Some subjects may have a record for high and low directions but
  # should be counted only once.
  denom <- length(unique(subj_cur_col))

  for (lvl in x_lvls) {
    if (lvl != "Any") {
      df_lvl <- df[df[[.var]] == lvl, ]
    } else {
      df_lvl <- df[df[[.var]] != 0, ]
    }
    num <- length(unique(df_lvl[[variables[["id"]]]]))
    fraction <- ifelse(denom == 0, 0, num / denom)
    result[[lvl]] <- formatters::with_label(c(count = num, fraction = fraction), lvl)
  }

  result <- list(count_fraction = result)
  result
}

#' @describeIn abnormal_by_worst_grade Formatted analysis function which is used as `afun`
#'   in `count_abnormal_by_worst_grade()`.
#'
#' @return
#' * `a_count_abnormal_by_worst_grade()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_by_worst_grade <- make_afun( # nolint
  s_count_abnormal_by_worst_grade,
  .formats = c(count_fraction = format_count_fraction)
)

#' @describeIn abnormal_by_worst_grade Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_by_worst_grade()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal_by_worst_grade()` to the table layout.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#' adlb <- tern_ex_adlb
#'
#' # Data is modified in order to have some parameters with grades only in one direction
#' # and simulate the real data.
#' adlb$ATOXGR[adlb$PARAMCD == "ALT" & adlb$ATOXGR %in% c("1", "2", "3", "4")] <- "-1"
#' adlb$ANRIND[adlb$PARAMCD == "ALT" & adlb$ANRIND == "HIGH"] <- "LOW"
#' adlb$WGRHIFL[adlb$PARAMCD == "ALT"] <- ""
#'
#' adlb$ATOXGR[adlb$PARAMCD == "IGA" & adlb$ATOXGR %in% c("-1", "-2", "-3", "-4")] <- "1"
#' adlb$ANRIND[adlb$PARAMCD == "IGA" & adlb$ANRIND == "LOW"] <- "HIGH"
#' adlb$WGRLOFL[adlb$PARAMCD == "IGA"] <- ""
#'
#' # Pre-processing
#' adlb_f <- adlb %>% h_adlb_abnormal_by_worst_grade()
#'
#' # Map excludes records without abnormal grade since they should not be displayed
#' # in the table.
#' map <- unique(adlb_f[adlb_f$GRADE_DIR != "ZERO", c("PARAM", "GRADE_DIR", "GRADE_ANL")]) %>%
#'   lapply(as.character) %>%
#'   as.data.frame() %>%
#'   arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   split_rows_by("PARAM") %>%
#'   split_rows_by("GRADE_DIR", split_fun = trim_levels_to_map(map)) %>%
#'   count_abnormal_by_worst_grade(
#'     var = "GRADE_ANL",
#'     variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
#'   ) %>%
#'   build_table(df = adlb_f)
#'
#' @export
#' @order 2
count_abnormal_by_worst_grade <- function(lyt,
                                          var,
                                          variables = list(
                                            id = "USUBJID",
                                            param = "PARAM",
                                            grade_dir = "GRADE_DIR"
                                          ),
                                          na_str = default_na_str(),
                                          nested = TRUE,
                                          ...,
                                          .stats = NULL,
                                          .formats = NULL,
                                          .labels = NULL,
                                          .indent_mods = NULL) {
  extra_args <- list(variables = variables, ...)

  afun <- make_afun(
    a_count_abnormal_by_worst_grade,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )
  analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = "hidden"
  )
}

#' Helper function to prepare `ADLB` for [count_abnormal_by_worst_grade()]
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to prepare an `ADLB` data frame to be used as input in
#' [count_abnormal_by_worst_grade()]. The following pre-processing steps are applied:
#'
#' 1. `adlb` is filtered on variable `avisit` to only include post-baseline visits.
#' 2. `adlb` is filtered on variables `worst_flag_low` and `worst_flag_high` so that only
#'    worst grades (in either direction) are included.
#' 3. From the standard lab grade variable `atoxgr`, the following two variables are derived
#'    and added to `adlb`:
#'   * A grade direction variable (e.g. `GRADE_DIR`). The variable takes value `"HIGH"` when
#'     `atoxgr > 0`, `"LOW"` when `atoxgr < 0`, and `"ZERO"` otherwise.
#'   * A toxicity grade variable (e.g. `GRADE_ANL`) where all negative values from `atoxgr` are
#'     replaced by their absolute values.
#' 4. Unused factor levels are dropped from `adlb` via [droplevels()].
#'
#' @param adlb (`data.frame`)\cr `ADLB` dataframe.
#' @param atoxgr (`character`)\cr Analysis toxicity grade variable. This must be a `factor`
#'   variable.
#' @param avisit (`character`)\cr Analysis visit variable.
#' @param worst_flag_low (`character`)\cr Worst low lab grade flag variable. This variable is
#'   set to `"Y"` when indicating records of worst low lab grades.
#' @param worst_flag_high (`character`)\cr Worst high lab grade flag variable. This variable is
#'   set to `"Y"` when indicating records of worst high lab grades.
#'
#' @return `h_adlb_abnormal_by_worst_grade()` returns the `adlb` data frame with two new
#'   variables: `GRADE_DIR` and `GRADE_ANL`.
#'
#' @seealso [abnormal_by_worst_grade]
#'
#' @examples
#' h_adlb_abnormal_by_worst_grade(tern_ex_adlb) %>%
#'   dplyr::select(ATOXGR, GRADE_DIR, GRADE_ANL) %>%
#'   head(10)
#'
#' @export
h_adlb_abnormal_by_worst_grade <- function(adlb,
                                           atoxgr = "ATOXGR",
                                           avisit = "AVISIT",
                                           worst_flag_low = "WGRLOFL",
                                           worst_flag_high = "WGRHIFL") {
  adlb %>%
    dplyr::filter(
      !.data[[avisit]] %in% c("SCREENING", "BASELINE"),
      .data[[worst_flag_low]] == "Y" | .data[[worst_flag_high]] == "Y"
    ) %>%
    dplyr::mutate(
      GRADE_DIR = factor(
        dplyr::case_when(
          .data[[atoxgr]] %in% c("-1", "-2", "-3", "-4") ~ "LOW",
          .data[[atoxgr]] == "0" ~ "ZERO",
          .data[[atoxgr]] %in% c("1", "2", "3", "4") ~ "HIGH"
        ),
        levels = c("LOW", "ZERO", "HIGH")
      ),
      GRADE_ANL = forcats::fct_relevel(
        forcats::fct_recode(.data[[atoxgr]], `1` = "-1", `2` = "-2", `3` = "-3", `4` = "-4"),
        c("0", "1", "2", "3", "4")
      )
    ) %>%
    droplevels()
}

#' Number of Patients
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Count the number of unique and non-unique patients in a column (variable).
#'
#' @inheritParams argument_convention
#' @param count_by (`vector`)\cr optional vector of any type to be combined with `x` when counting `nonunique`
#'   records.
#' @param unique_count_suffix (`logical`)\cr should `"(n)"` suffix be added to `unique_count` labels.
#'   Defaults to `TRUE`.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("summarize_num_patients")`
#'   to see available statistics for this function.
#'
#' @name summarize_num_patients
#' @order 1
NULL

#' @describeIn summarize_num_patients Statistics function which counts the number of
#'   unique patients, the corresponding percentage taken with respect to the
#'   total number of patients, and the number of non-unique patients.
#'
#' @param x (`character` or `factor`)\cr vector of patient IDs.
#'
#' @return
#' * `s_num_patients()` returns a named `list` of 3 statistics:
#'   * `unique`: Vector of counts and percentages.
#'   * `nonunique`: Vector of counts.
#'   * `unique_count`: Counts.
#'
#' @examples
#' # Use the statistics function to count number of unique and nonunique patients.
#' s_num_patients(x = as.character(c(1, 1, 1, 2, 4, NA)), labelstr = "", .N_col = 6L)
#' s_num_patients(
#'   x = as.character(c(1, 1, 1, 2, 4, NA)),
#'   labelstr = "",
#'   .N_col = 6L,
#'   count_by = c(1, 1, 2, 1, 1, 1)
#' )
#'
#' @export
s_num_patients <- function(x, labelstr, .N_col, count_by = NULL, unique_count_suffix = TRUE) { # nolint

  checkmate::assert_string(labelstr)
  checkmate::assert_count(.N_col)
  checkmate::assert_multi_class(x, classes = c("factor", "character"))
  checkmate::assert_flag(unique_count_suffix)

  count1 <- n_available(unique(x))
  count2 <- n_available(x)

  if (!is.null(count_by)) {
    checkmate::assert_vector(count_by, len = length(x))
    count2 <- n_available(unique(interaction(x, count_by)))
  }

  out <- list(
    unique = formatters::with_label(c(count1, ifelse(count1 == 0 && .N_col == 0, 0, count1 / .N_col)), labelstr),
    nonunique = formatters::with_label(count2, labelstr),
    unique_count = formatters::with_label(
      count1, ifelse(unique_count_suffix, paste0(labelstr, if (nzchar(labelstr)) " ", "(n)"), labelstr)
    )
  )

  out
}

#' @describeIn summarize_num_patients Statistics function which counts the number of unique patients
#'   in a column (variable), the corresponding percentage taken with respect to the total number of
#'   patients, and the number of non-unique patients in the column.
#'
#' @param required (`character` or `NULL`)\cr optional name of a variable that is required to be non-missing.
#'
#' @return
#' * `s_num_patients_content()` returns the same values as `s_num_patients()`.
#'
#' @examples
#' # Count number of unique and non-unique patients.
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(1, 2, 1, 4, NA)),
#'   EVENT = as.character(c(10, 15, 10, 17, 8))
#' )
#' s_num_patients_content(df, .N_col = 5, .var = "USUBJID")
#'
#' df_by_event <- data.frame(
#'   USUBJID = as.character(c(1, 2, 1, 4, NA)),
#'   EVENT = c(10, 15, 10, 17, 8)
#' )
#' s_num_patients_content(df_by_event, .N_col = 5, .var = "USUBJID", count_by = "EVENT")
#'
#' @export
s_num_patients_content <- function(df,
                                   labelstr = "",
                                   .N_col, # nolint
                                   .var,
                                   required = NULL,
                                   count_by = NULL,
                                   unique_count_suffix = TRUE) {
  checkmate::assert_string(.var)
  checkmate::assert_data_frame(df)
  if (is.null(count_by)) {
    assert_df_with_variables(df, list(id = .var))
  } else {
    assert_df_with_variables(df, list(id = .var, count_by = count_by))
  }
  if (!is.null(required)) {
    checkmate::assert_string(required)
    assert_df_with_variables(df, list(required = required))
    df <- df[!is.na(df[[required]]), , drop = FALSE]
  }

  x <- df[[.var]]
  y <- if (is.null(count_by)) NULL else df[[count_by]]

  s_num_patients(
    x = x,
    labelstr = labelstr,
    .N_col = .N_col,
    count_by = y,
    unique_count_suffix = unique_count_suffix
  )
}

c_num_patients <- make_afun(
  s_num_patients_content,
  .stats = c("unique", "nonunique", "unique_count"),
  .formats = c(unique = format_count_fraction_fixed_dp, nonunique = "xx", unique_count = "xx")
)

#' @describeIn summarize_num_patients Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_num_patients()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_num_patients_content()` to the table layout.
#'
#' @export
#' @order 3
summarize_num_patients <- function(lyt,
                                   var,
                                   required = NULL,
                                   count_by = NULL,
                                   unique_count_suffix = TRUE,
                                   na_str = default_na_str(),
                                   .stats = NULL,
                                   .formats = NULL,
                                   .labels = c(
                                     unique = "Number of patients with at least one event",
                                     nonunique = "Number of events"
                                   ),
                                   .indent_mods = 0L,
                                   riskdiff = FALSE,
                                   ...) {
  checkmate::assert_flag(riskdiff)

  if (is.null(.stats)) .stats <- c("unique", "nonunique", "unique_count")
  if (length(.labels) > length(.stats)) .labels <- .labels[names(.labels) %in% .stats]

  s_args <- list(required = required, count_by = count_by, unique_count_suffix = unique_count_suffix, ...)

  cfun <- make_afun(
    c_num_patients,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels
  )

  extra_args <- if (isFALSE(riskdiff)) {
    s_args
  } else {
    list(
      afun = list("s_num_patients_content" = cfun),
      .stats = .stats,
      .indent_mods = .indent_mods,
      s_args = s_args
    )
  }

  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = ifelse(isFALSE(riskdiff), cfun, afun_riskdiff),
    na_str = na_str,
    extra_args = extra_args,
    indent_mod = .indent_mods
  )
}

#' @describeIn summarize_num_patients Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `analyze_num_patients()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_num_patients_content()` to the table layout.
#'
#' @details In general, functions that starts with `analyze*` are expected to
#'   work like [rtables::analyze()], while functions that starts with `summarize*`
#'   are based upon [rtables::summarize_row_groups()]. The latter provides a
#'   value for each dividing split in the row and column space, but, being it
#'   bound to the fundamental splits, it is repeated by design in every page
#'   when pagination is involved.
#'
#' @note As opposed to [summarize_num_patients()], this function does not repeat the produced rows.
#'
#' @examples
#' df <- data.frame(
#'   USUBJID = as.character(c(1, 2, 1, 4, NA, 6, 6, 8, 9)),
#'   ARM = c("A", "A", "A", "A", "A", "B", "B", "B", "B"),
#'   AGE = c(10, 15, 10, 17, 8, 11, 11, 19, 17)
#' )
#'
#' tbl <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   analyze_num_patients("USUBJID", .stats = c("unique")) %>%
#'   build_table(df)
#'
#' tbl
#'
#' @export
#' @order 2
analyze_num_patients <- function(lyt,
                                 vars,
                                 required = NULL,
                                 count_by = NULL,
                                 unique_count_suffix = TRUE,
                                 na_str = default_na_str(),
                                 nested = TRUE,
                                 .stats = NULL,
                                 .formats = NULL,
                                 .labels = c(
                                   unique = "Number of patients with at least one event",
                                   nonunique = "Number of events"
                                 ),
                                 show_labels = c("default", "visible", "hidden"),
                                 .indent_mods = 0L,
                                 riskdiff = FALSE,
                                 ...) {
  checkmate::assert_flag(riskdiff)

  if (is.null(.stats)) .stats <- c("unique", "nonunique", "unique_count")
  if (length(.labels) > length(.stats)) .labels <- .labels[names(.labels) %in% .stats]

  s_args <- list(required = required, count_by = count_by, unique_count_suffix = unique_count_suffix, ...)

  afun <- make_afun(
    c_num_patients,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels
  )

  extra_args <- if (isFALSE(riskdiff)) {
    s_args
  } else {
    list(
      afun = list("s_num_patients_content" = afun),
      .stats = .stats,
      .indent_mods = .indent_mods,
      s_args = s_args
    )
  }

  analyze(
    afun = ifelse(isFALSE(riskdiff), afun, afun_riskdiff),
    lyt = lyt,
    vars = vars,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    indent_mod = .indent_mods
  )
}

#' Formatting Functions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' See below for the list of formatting functions created in `tern` to work with `rtables`.
#'
#' Other available formats can be listed via [`formatters::list_valid_format_labels()`]. Additional
#' custom formats can be created via the [`formatters::sprintf_format()`] function.
#'
#' @family formatting functions
#' @name formatting_functions
NULL

#' Formatting Fraction and Percentage
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a fraction together with ratio in percent.
#'
#' @param x (`integer`)\cr with elements `num` and `denom`.
#' @param ... required for `rtables` interface.
#'
#' @return A string in the format `num / denom (ratio %)`. If `num` is 0, the format is `num / denom`.
#'
#' @examples
#' format_fraction(x = c(num = 2L, denom = 3L))
#' format_fraction(x = c(num = 0L, denom = 3L))
#'
#' @family formatting functions
#' @export
format_fraction <- function(x, ...) {
  attr(x, "label") <- NULL

  checkmate::assert_vector(x)
  checkmate::assert_count(x["num"])
  checkmate::assert_count(x["denom"])

  result <- if (x["num"] == 0) {
    paste0(x["num"], "/", x["denom"])
  } else {
    paste0(
      x["num"], "/", x["denom"],
      " (", round(x["num"] / x["denom"] * 100, 1), "%)"
    )
  }

  return(result)
}

#' Formatting Fraction and Percentage with Fixed Single Decimal Place
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a fraction together with ratio in percent with fixed single decimal place.
#' Includes trailing zero in case of whole number percentages to always keep one decimal place.
#'
#' @param x (`integer`)\cr with elements `num` and `denom`.
#' @param ... required for `rtables` interface.
#'
#' @return A string in the format `num / denom (ratio %)`. If `num` is 0, the format is `num / denom`.
#'
#' @examples
#' format_fraction_fixed_dp(x = c(num = 1L, denom = 2L))
#' format_fraction_fixed_dp(x = c(num = 1L, denom = 4L))
#' format_fraction_fixed_dp(x = c(num = 0L, denom = 3L))
#'
#' @family formatting functions
#' @export
format_fraction_fixed_dp <- function(x, ...) {
  attr(x, "label") <- NULL
  checkmate::assert_vector(x)
  checkmate::assert_count(x["num"])
  checkmate::assert_count(x["denom"])

  result <- if (x["num"] == 0) {
    paste0(x["num"], "/", x["denom"])
  } else {
    paste0(
      x["num"], "/", x["denom"],
      " (", sprintf("%.1f", round(x["num"] / x["denom"] * 100, 1)), "%)"
    )
  }
  return(result)
}

#' Formatting Count and Fraction
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a count together with fraction with special consideration when count is `0`.
#'
#' @param x (`integer`)\cr vector of length 2, count and fraction.
#' @param ... required for `rtables` interface.
#'
#' @return A string in the format `count (fraction %)`. If `count` is 0, the format is `0`.
#'
#' @examples
#' format_count_fraction(x = c(2, 0.6667))
#' format_count_fraction(x = c(0, 0))
#'
#' @family formatting functions
#' @export
format_count_fraction <- function(x, ...) {
  attr(x, "label") <- NULL

  if (any(is.na(x))) {
    return("NA")
  }

  checkmate::assert_vector(x)
  checkmate::assert_integerish(x[1])
  assert_proportion_value(x[2], include_boundaries = TRUE)

  result <- if (x[1] == 0) {
    "0"
  } else {
    paste0(x[1], " (", round(x[2] * 100, 1), "%)")
  }

  return(result)
}

#' Formatting Count and Percentage with Fixed Single Decimal Place
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Formats a count together with fraction with special consideration when count is `0`.
#'
#' @param x (`integer`)\cr vector of length 2, count and fraction.
#' @param ... required for `rtables` interface.
#'
#' @return A string in the format `count (fraction %)`. If `count` is 0, the format is `0`.
#'
#' @examples
#' format_count_fraction_fixed_dp(x = c(2, 0.6667))
#' format_count_fraction_fixed_dp(x = c(2, 0.5))
#' format_count_fraction_fixed_dp(x = c(0, 0))
#'
#' @family formatting functions
#' @export
format_count_fraction_fixed_dp <- function(x, ...) {
  attr(x, "label") <- NULL

  if (any(is.na(x))) {
    return("NA")
  }

  checkmate::assert_vector(x)
  checkmate::assert_integerish(x[1])
  assert_proportion_value(x[2], include_boundaries = TRUE)

  result <- if (x[1] == 0) {
    "0"
  } else if (.is_equal_float(x[2], 1)) {
    sprintf("%d (100%%)", x[1])
  } else {
    sprintf("%d (%.1f%%)", x[1], x[2] * 100)
  }

  return(result)
}

#' Formatting Count and Fraction with Special Case for Count < 10
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a count together with fraction with special consideration when count is less than 10.
#'
#' @inheritParams format_count_fraction
#'
#' @return A string in the format `count (fraction %)`. If `count` is less than 10, only `count` is printed.
#'
#' @examples
#' format_count_fraction_lt10(x = c(275, 0.9673))
#' format_count_fraction_lt10(x = c(2, 0.6667))
#' format_count_fraction_lt10(x = c(9, 1))
#'
#' @family formatting functions
#' @export
format_count_fraction_lt10 <- function(x, ...) {
  attr(x, "label") <- NULL

  if (any(is.na(x))) {
    return("NA")
  }

  checkmate::assert_vector(x)
  checkmate::assert_integerish(x[1])
  assert_proportion_value(x[2], include_boundaries = TRUE)

  result <- if (x[1] < 10) {
    paste0(x[1])
  } else {
    paste0(x[1], " (", round(x[2] * 100, 1), "%)")
  }

  return(result)
}

#' Formatting: XX as Formatting Function
#'
#' Translate a string where x and dots are interpreted as number place
#' holders, and others as formatting elements.
#'
#' @param str (`string`)\cr template.
#'
#' @return An `rtables` formatting function.
#'
#' @examples
#' test <- list(c(1.658, 0.5761), c(1e1, 785.6))
#'
#' z <- format_xx("xx (xx.x)")
#' sapply(test, z)
#'
#' z <- format_xx("xx.x - xx.x")
#' sapply(test, z)
#'
#' z <- format_xx("xx.x, incl. xx.x% NE")
#' sapply(test, z)
#'
#' @family formatting functions
#' @export
format_xx <- function(str) {
  # Find position in the string.
  positions <- gregexpr(pattern = "x+\\.x+|x+", text = str, perl = TRUE)
  x_positions <- regmatches(x = str, m = positions)[[1]]

  # Roundings depends on the number of x behind [.].
  roundings <- lapply(
    X = x_positions,
    function(x) {
      y <- strsplit(split = "\\.", x = x)[[1]]
      rounding <- function(x) {
        round(x, digits = ifelse(length(y) > 1, nchar(y[2]), 0))
      }
      return(rounding)
    }
  )

  rtable_format <- function(x, output) {
    values <- Map(y = x, fun = roundings, function(y, fun) fun(y))
    regmatches(x = str, m = positions)[[1]] <- values
    return(str)
  }

  return(rtable_format)
}

#' Formatting Numeric Values By Significant Figures
#'
#' Format numeric values to print with a specified number of significant figures.
#'
#' @param sigfig (`integer`)\cr number of significant figures to display.
#' @param format (`character`)\cr the format label (string) to apply when printing the value. Decimal
#'   places in string are ignored in favor of formatting by significant figures. Formats options are:
#'   `"xx"`, `"xx / xx"`, `"(xx, xx)"`, `"xx - xx"`, and `"xx (xx)"`.
#' @param num_fmt (`character`)\cr numeric format modifiers to apply to the value. Defaults to `"fg"` for
#'   standard significant figures formatting - fixed (non-scientific notation) format (`"f"`)
#'   and `sigfig` equal to number of significant figures instead of decimal places (`"g"`). See the
#'   [formatC()] `format` argument for more options.
#'
#' @return An `rtables` formatting function.
#'
#' @examples
#' fmt_3sf <- format_sigfig(3)
#' fmt_3sf(1.658)
#' fmt_3sf(1e1)
#'
#' fmt_5sf <- format_sigfig(5)
#' fmt_5sf(0.57)
#' fmt_5sf(0.000025645)
#'
#' @family formatting functions
#' @export
format_sigfig <- function(sigfig, format = "xx", num_fmt = "fg") {
  checkmate::assert_integerish(sigfig)
  format <- gsub("xx\\.|xx\\.x+", "xx", format)
  checkmate::assert_choice(format, c("xx", "xx / xx", "(xx, xx)", "xx - xx", "xx (xx)"))
  function(x, ...) {
    if (!is.numeric(x)) stop("`format_sigfig` cannot be used for non-numeric values. Please choose another format.")
    num <- formatC(signif(x, digits = sigfig), digits = sigfig, format = num_fmt, flag = "#")
    num <- gsub("\\.$", "", num) # remove trailing "."

    format_value(num, format)
  }
}

#' Formatting Fraction with Lower Threshold
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a fraction when the second element of the input `x` is the fraction. It applies
#' a lower threshold, below which it is just stated that the fraction is smaller than that.
#'
#' @param threshold (`proportion`)\cr lower threshold.
#'
#' @return An `rtables` formatting function that takes numeric input `x` where the second
#'   element is the fraction that is formatted. If the fraction is above or equal to the threshold,
#'   then it is displayed in percentage. If it is positive but below the threshold, it returns,
#'   e.g. "<1" if the threshold is `0.01`. If it is zero, then just "0" is returned.
#'
#' @examples
#' format_fun <- format_fraction_threshold(0.05)
#' format_fun(x = c(20, 0.1))
#' format_fun(x = c(2, 0.01))
#' format_fun(x = c(0, 0))
#'
#' @family formatting functions
#' @export
format_fraction_threshold <- function(threshold) {
  assert_proportion_value(threshold)
  string_below_threshold <- paste0("<", round(threshold * 100))
  function(x, ...) {
    assert_proportion_value(x[2], include_boundaries = TRUE)
    ifelse(
      x[2] > 0.01,
      round(x[2] * 100),
      ifelse(
        x[2] == 0,
        "0",
        string_below_threshold
      )
    )
  }
}

#' Formatting Extreme Values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' `rtables` formatting functions that handle extreme values.
#'
#' @param digits (`integer`)\cr number of decimal places to display.
#'
#' @details For each input, apply a format to the specified number of `digits`. If the value is
#'    below a threshold, it returns "<0.01" e.g. if the number of `digits` is 2. If the value is
#'    above a threshold, it returns ">999.99" e.g. if the number of `digits` is 2.
#'    If it is zero, then returns "0.00".
#'
#' @family formatting functions
#' @name extreme_format
NULL

#' @describeIn extreme_format Internal helper function to calculate the threshold and create formatted strings
#'  used in Formatting Functions. Returns a list with elements `threshold` and `format_string`.
#'
#' @return
#' * `h_get_format_threshold()` returns a `list` of 2 elements: `threshold`, with `low` and `high` thresholds,
#'   and `format_string`, with thresholds formatted as strings.
#'
#' @examples
#' h_get_format_threshold(2L)
#'
#' @export
h_get_format_threshold <- function(digits = 2L) {
  checkmate::assert_integerish(digits)

  low_threshold <- 1 / (10 ^ digits) # styler: off
  high_threshold <- 1000 - (1 / (10 ^ digits)) # styler: off

  string_below_threshold <- paste0("<", low_threshold)
  string_above_threshold <- paste0(">", high_threshold)

  list(
    "threshold" = c(low = low_threshold, high = high_threshold),
    "format_string" = c(low = string_below_threshold, high = string_above_threshold)
  )
}

#' @describeIn extreme_format Internal helper function to apply a threshold format to a value.
#'   Creates a formatted string to be used in Formatting Functions.
#'
#' @param x (`number`)\cr value to format.
#'
#' @return
#' * `h_format_threshold()` returns the given value, or if the value is not within the digit threshold the relation
#'   of the given value to the digit threshold, as a formatted string.
#'
#' @examples
#' h_format_threshold(0.001)
#' h_format_threshold(1000)
#'
#' @export
h_format_threshold <- function(x, digits = 2L) {
  if (is.na(x)) {
    return(x)
  }

  checkmate::assert_numeric(x, lower = 0)

  l_fmt <- h_get_format_threshold(digits)

  result <- if (x < l_fmt$threshold["low"] && 0 < x) {
    l_fmt$format_string["low"]
  } else if (x > l_fmt$threshold["high"]) {
    l_fmt$format_string["high"]
  } else {
    sprintf(fmt = paste0("%.", digits, "f"), x)
  }

  unname(result)
}

#' Formatting a Single Extreme Value
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Create Formatting Function for a single extreme value.
#'
#' @inheritParams extreme_format
#'
#' @return An `rtables` formatting function that uses threshold `digits` to return a formatted extreme value.
#'
#' @examples
#' format_fun <- format_extreme_values(2L)
#' format_fun(x = 0.127)
#' format_fun(x = Inf)
#' format_fun(x = 0)
#' format_fun(x = 0.009)
#'
#' @family formatting functions
#' @export
format_extreme_values <- function(digits = 2L) {
  function(x, ...) {
    checkmate::assert_scalar(x, na.ok = TRUE)

    h_format_threshold(x = x, digits = digits)
  }
}

#' Formatting Extreme Values Part of a Confidence Interval
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formatting Function for extreme values part of a confidence interval. Values
#' are formatted as e.g. "(xx.xx, xx.xx)" if the number of `digits` is 2.
#'
#' @inheritParams extreme_format
#'
#' @return An `rtables` formatting function that uses threshold `digits` to return a formatted extreme
#'   values confidence interval.
#'
#' @examples
#' format_fun <- format_extreme_values_ci(2L)
#' format_fun(x = c(0.127, Inf))
#' format_fun(x = c(0, 0.009))
#'
#' @family formatting functions
#' @export
format_extreme_values_ci <- function(digits = 2L) {
  function(x, ...) {
    checkmate::assert_vector(x, len = 2)
    l_result <- h_format_threshold(x = x[1], digits = digits)
    h_result <- h_format_threshold(x = x[2], digits = digits)

    paste0("(", l_result, ", ", h_result, ")")
  }
}

#' Automatic formats from data significant digits
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formatting function for the majority of default methods used in [analyze_vars()].
#' For non-derived values, the significant digits of data is used (e.g. range), while derived
#' values have one more digits (measure of location and dispersion like mean, standard deviation).
#' This function can be called internally with "auto" like, for example,
#' `.formats = c("mean" = "auto")`. See details to see how this works with the inner function.
#'
#' @param dt_var (`numeric`) \cr all the data the statistics was created upon. Used only to find
#'   significant digits. In [analyze_vars] this comes from `.df_row` (see
#'   [rtables::additional_fun_params]), and it is the row data after the above row splits. No
#'   column split is considered.
#' @param x_stat (`string`) \cr string indicating the current statistical method used.
#'
#' @return A string that `rtables` prints in a table cell.
#'
#' @details
#' The internal function is needed to work with `rtables` default structure for
#' format functions, i.e. `function(x, ...)`, where is x are results from statistical evaluation.
#' It can be more than one element (e.g. for `.stats = "mean_sd"`).
#'
#' @examples
#' x_todo <- c(0.001, 0.2, 0.0011000, 3, 4)
#' res <- c(mean(x_todo[1:3]), sd(x_todo[1:3]))
#'
#' # x is the result coming into the formatting function -> res!!
#' format_auto(dt_var = x_todo, x_stat = "mean_sd")(x = res)
#' format_auto(x_todo, "range")(x = range(x_todo))
#' no_sc_x <- c(0.0000001, 1)
#' format_auto(no_sc_x, "range")(x = no_sc_x)
#'
#' @family formatting functions
#' @export
format_auto <- function(dt_var, x_stat) {
  function(x = "", ...) {
    checkmate::assert_numeric(x, min.len = 1)
    checkmate::assert_numeric(dt_var, min.len = 1)
    # Defaults - they may be a param in the future
    der_stats <- c(
      "mean", "sd", "se", "median", "geom_mean", "quantiles", "iqr",
      "mean_sd", "mean_se", "mean_se", "mean_ci", "mean_sei", "mean_sdi",
      "median_ci"
    )
    nonder_stats <- c("n", "range", "min", "max")

    # Safenet for miss-modifications
    stopifnot(length(intersect(der_stats, nonder_stats)) == 0) # nolint
    checkmate::assert_choice(x_stat, c(der_stats, nonder_stats))

    # Finds the max number of digits in data
    detect_dig <- vapply(dt_var, count_decimalplaces, FUN.VALUE = numeric(1)) %>%
      max()

    if (x_stat %in% der_stats) {
      detect_dig <- detect_dig + 1
    }

    # Render input
    str_vals <- formatC(x, digits = detect_dig, format = "f")
    def_fmt <- get_formats_from_stats(x_stat)[[x_stat]]
    str_fmt <- str_extract(def_fmt, invert = FALSE)[[1]]
    if (length(str_fmt) != length(str_vals)) {
      stop(
        "Number of inserted values as result (", length(str_vals),
        ") is not the same as there should be in the default tern formats for ",
        x_stat, " (-> ", def_fmt, " needs ", length(str_fmt), " values). ",
        "See tern_default_formats to check all of them."
      )
    }

    # Squashing them together
    inv_str_fmt <- str_extract(def_fmt, invert = TRUE)[[1]]
    stopifnot(length(inv_str_fmt) == length(str_vals) + 1) # nolint

    out <- vector("character", length = length(inv_str_fmt) + length(str_vals))
    is_even <- seq_along(out) %% 2 == 0
    out[is_even] <- str_vals
    out[!is_even] <- inv_str_fmt

    return(paste0(out, collapse = ""))
  }
}

# Utility function that could be useful in general
str_extract <- function(string, pattern = "xx|xx\\.|xx\\.x+", invert = FALSE) {
  regmatches(string, gregexpr(pattern, string), invert = invert)
}

# Helper function
count_decimalplaces <- function(dec) {
  if (abs(dec - round(dec)) > .Machine$double.eps^0.5) { # For precision
    nchar(strsplit(format(dec, scientific = FALSE, trim = FALSE), ".", fixed = TRUE)[[1]][[2]])
  } else {
    return(0)
  }
}

#' Apply Auto Formatting
#'
#' Checks if any of the listed formats in `.formats` are `"auto"`, and replaces `"auto"` with
#' the correct implementation of `format_auto` for the given statistics, data, and variable.
#'
#' @inheritParams argument_convention
#' @param x_stats (named `list`)\cr a named list of statistics where each element corresponds
#'   to an element in `.formats`, with matching names.
#'
#' @keywords internal
apply_auto_formatting <- function(.formats, x_stats, .df_row, .var) {
  is_auto_fmt <- vapply(.formats, function(ii) is.character(ii) && ii == "auto", logical(1))
  if (any(is_auto_fmt)) {
    auto_stats <- x_stats[is_auto_fmt]
    var_df <- .df_row[[.var]] # xxx this can be extended for the WHOLE data or single facets
    .formats[is_auto_fmt] <- lapply(names(auto_stats), format_auto, dt_var = var_df)
  }
  .formats
}

#' Occurrence Table Sorting
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions to score occurrence table subtables and rows which can be used in the
#' sorting of occurrence tables.
#'
#' @name score_occurrences
NULL

#' @describeIn score_occurrences Scoring function which sums the counts across all
#'   columns. It will fail if anything else but counts are used.
#'
#' @inheritParams rtables_access
#'
#' @return
#' * `score_occurrences()` returns the sum of counts across all columns of a table row.
#'
#' @seealso [h_row_first_values()]
#'
#' @examples
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   analyze_num_patients(
#'     vars = "USUBJID",
#'     .stats = c("unique"),
#'     .labels = c("Total number of patients with at least one event")
#'   ) %>%
#'   split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE) %>%
#'   summarize_num_patients(
#'     var = "USUBJID",
#'     .stats = c("unique", "nonunique"),
#'     .labels = c(
#'       "Total number of patients with at least one event",
#'       "Total number of events"
#'     )
#'   ) %>%
#'   count_occurrences(vars = "AEDECOD")
#'
#' tbl <- build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) %>%
#'   prune_table()
#'
#' tbl_sorted <- tbl %>%
#'   sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_occurrences)
#'
#' tbl_sorted
#'
#' @export
score_occurrences <- function(table_row) {
  row_counts <- h_row_counts(table_row)
  sum(row_counts)
}

#' @describeIn score_occurrences Scoring functions can be produced by this constructor to only include
#'   specific columns in the scoring. See [h_row_counts()] for further information.
#'
#' @inheritParams has_count_in_cols
#'
#' @return
#' * `score_occurrences_cols()` returns a function that sums counts across all specified columns
#'   of a table row.
#'
#' @seealso [h_row_counts()]
#'
#' @examples
#' score_cols_a_and_b <- score_occurrences_cols(col_names = c("A: Drug X", "B: Placebo"))
#'
#' # Note that this here just sorts the AEDECOD inside the AEBODSYS. The AEBODSYS are not sorted.
#' # That would require a second pass of `sort_at_path`.
#' tbl_sorted <- tbl %>%
#'   sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_cols_a_and_b)
#'
#' tbl_sorted
#'
#' @export
score_occurrences_cols <- function(...) {
  function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    sum(row_counts)
  }
}

#' @describeIn score_occurrences Scoring functions produced by this constructor can be used on
#'   subtables: They sum up all specified column counts in the subtable. This is useful when
#'   there is no available content row summing up these counts.
#'
#' @return
#' * `score_occurrences_subtable()` returns a function that sums counts in each subtable
#'   across all specified columns.
#'
#' @examples
#' score_subtable_all <- score_occurrences_subtable(col_names = names(tbl))
#'
#' # Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That
#' # would require a second pass of `sort_at_path`.
#' tbl_sorted <- tbl %>%
#'   sort_at_path(path = c("AEBODSYS"), scorefun = score_subtable_all, decreasing = FALSE)
#'
#' tbl_sorted
#'
#' @export
score_occurrences_subtable <- function(...) {
  score_table_row <- score_occurrences_cols(...)
  function(table_tree) {
    table_rows <- collect_leaves(table_tree)
    counts <- vapply(table_rows, score_table_row, numeric(1))
    sum(counts)
  }
}

#' @describeIn score_occurrences Produce score function for sorting table by summing the first content row in
#'   specified columns. Note that this is extending [rtables::cont_n_onecol()] and [rtables::cont_n_allcols()].
#'
#' @return
#' * `score_occurrences_cont_cols()` returns a function that sums counts in the first content row in
#'   specified columns.
#'
#' @export
score_occurrences_cont_cols <- function(...) {
  score_table_row <- score_occurrences_cols(...)
  function(table_tree) {
    if (inherits(table_tree, "ContentRow")) {
      return(NA)
    }
    content_row <- h_content_first_row(table_tree)
    score_table_row(content_row)
  }
}

#' Helper Function for Tabulation of a Single Biomarker Result
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Please see [h_tab_surv_one_biomarker()] and [h_tab_rsp_one_biomarker()], which use this function for examples.
#' This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @inheritParams argument_convention
#' @param df (`data.frame`)\cr results for a single biomarker.
#' @param afuns (named `list` of `function`)\cr analysis functions.
#' @param colvars (`list` with `vars` and `labels`)\cr variables to tabulate and their labels.
#'
#' @return An `rtables` table object with statistics in columns.
#'
#' @export
h_tab_one_biomarker <- function(df,
                                afuns,
                                colvars,
                                na_str = default_na_str(),
                                .indent_mods = 0L,
                                ...) {
  extra_args <- list(...)

  lyt <- basic_table()

  # Row split by row type - only keep the content rows here.
  lyt <- split_rows_by(
    lyt = lyt,
    var = "row_type",
    split_fun = keep_split_levels("content"),
    nested = FALSE
  )

  # Summarize rows with all patients.
  lyt <- summarize_row_groups(
    lyt = lyt,
    var = "var_label",
    cfun = afuns,
    na_str = na_str,
    indent_mod = .indent_mods,
    extra_args = extra_args
  )

  # Split cols by the multiple variables to populate into columns.
  lyt <- split_cols_by_multivar(
    lyt = lyt,
    vars = colvars$vars,
    varlabels = colvars$labels
  )

  # If there is any subgroup variables, we extend the layout accordingly.
  if ("analysis" %in% df$row_type) {
    # Now only continue with the subgroup rows.
    lyt <- split_rows_by(
      lyt = lyt,
      var = "row_type",
      split_fun = keep_split_levels("analysis"),
      nested = FALSE,
      child_labels = "hidden"
    )

    # Split by the subgroup variable.
    lyt <- split_rows_by(
      lyt = lyt,
      var = "var",
      labels_var = "var_label",
      nested = TRUE,
      child_labels = "visible",
      indent_mod = .indent_mods * 2
    )

    # Then analyze colvars for each subgroup.
    lyt <- summarize_row_groups(
      lyt = lyt,
      cfun = afuns,
      var = "subgroup",
      na_str = na_str,
      extra_args = extra_args
    )
  }
  build_table(lyt, df = df)
}

#' Summarize the Change from Baseline or Absolute Baseline Values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The primary analysis variable `.var` indicates the numerical change from baseline results,
#' and additional required secondary analysis variables are `value` and `baseline_flag`.
#' Depending on the baseline flag, either the absolute baseline values (at baseline)
#' or the change from baseline values (post-baseline) are then summarized.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("analyze_vars_numeric)`
#'   to see available statistics for this function.
#'
#' @name summarize_change
#' @order 1
NULL

#' @describeIn summarize_change Statistics function that summarizes baseline or post-baseline visits.
#'
#' @return
#' * `s_change_from_baseline()` returns the same values returned by [s_summary.numeric()].
#'
#' @note The data in `df` must be either all be from baseline or post-baseline visits. Otherwise
#'   an error will be thrown.
#'
#' @keywords internal
s_change_from_baseline <- function(df,
                                   .var,
                                   variables,
                                   na.rm = TRUE, # nolint
                                   ...) {
  checkmate::assert_numeric(df[[variables$value]])
  checkmate::assert_numeric(df[[.var]])
  checkmate::assert_logical(df[[variables$baseline_flag]])
  checkmate::assert_vector(unique(df[[variables$baseline_flag]]), max.len = 1)
  assert_df_with_variables(df, c(variables, list(chg = .var)))

  combined <- ifelse(
    df[[variables$baseline_flag]],
    df[[variables$value]],
    df[[.var]]
  )
  if (is.logical(combined) && identical(length(combined), 0L)) {
    combined <- numeric(0)
  }
  s_summary(combined, na.rm = na.rm, ...)
}

#' @describeIn summarize_change Formatted analysis function which is used as `afun` in `summarize_change()`.
#'
#' @return
#' * `a_change_from_baseline()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_change_from_baseline <- make_afun(
  s_change_from_baseline,
  .formats = c(
    n = "xx",
    mean_sd = "xx.xx (xx.xx)",
    mean_se = "xx.xx (xx.xx)",
    median = "xx.xx",
    range = "xx.xx - xx.xx",
    mean_ci = "(xx.xx, xx.xx)",
    median_ci = "(xx.xx, xx.xx)",
    mean_pval = "xx.xx"
  ),
  .labels = c(
    mean_sd = "Mean (SD)",
    mean_se = "Mean (SE)",
    median = "Median",
    range = "Min - Max"
  )
)

#' @describeIn summarize_change Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `summarize_change()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_change_from_baseline()` to the table layout.
#'
#' @note To be used after a split on visits in the layout, such that each data subset only contains
#'   either baseline or post-baseline data.
#'
#' @examples
#' library(dplyr)
#'
#' ## Fabricate dataset
#' dta_test <- data.frame(
#'   USUBJID = rep(1:6, each = 3),
#'   AVISIT = rep(paste0("V", 1:3), 6),
#'   ARM = rep(LETTERS[1:3], rep(6, 3)),
#'   AVAL = c(9:1, rep(NA, 9))
#' ) %>%
#'   mutate(ABLFLL = AVISIT == "V1") %>%
#'   group_by(USUBJID) %>%
#'   mutate(
#'     BLVAL = AVAL[ABLFLL],
#'     CHG = AVAL - BLVAL
#'   ) %>%
#'   ungroup()
#'
#' results <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   summarize_change("CHG", variables = list(value = "AVAL", baseline_flag = "ABLFLL")) %>%
#'   build_table(dta_test)
#'
#' results
#'
#' @export
#' @order 2
summarize_change <- function(lyt,
                             vars,
                             variables,
                             na_str = default_na_str(),
                             nested = TRUE,
                             ...,
                             table_names = vars,
                             .stats = c("n", "mean_sd", "median", "range"),
                             .formats = NULL,
                             .labels = NULL,
                             .indent_mods = NULL) {
  extra_args <- list(variables = variables, ...)

  afun <- make_afun(
    a_change_from_baseline,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    table_names = table_names
  )
}

#' Stack Multiple Grobs
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Stack grobs as a new grob with 1 column and multiple rows layout.
#'
#' @param ... grobs.
#' @param grobs list of grobs.
#' @param padding unit of length 1, space between each grob.
#' @param vp a [viewport()] object (or `NULL`).
#' @param name a character identifier for the grob.
#' @param gp A [gpar()] object.
#'
#' @return A `grob`.
#'
#' @examples
#' library(grid)
#'
#' g1 <- circleGrob(gp = gpar(col = "blue"))
#' g2 <- circleGrob(gp = gpar(col = "red"))
#' g3 <- textGrob("TEST TEXT")
#' grid.newpage()
#' grid.draw(stack_grobs(g1, g2, g3))
#'
#' showViewport()
#'
#' grid.newpage()
#' pushViewport(viewport(layout = grid.layout(1, 2)))
#' vp1 <- viewport(layout.pos.row = 1, layout.pos.col = 2)
#' grid.draw(stack_grobs(g1, g2, g3, vp = vp1, name = "test"))
#'
#' showViewport()
#' grid.ls(grobs = TRUE, viewports = TRUE, print = FALSE)
#'
#' @export
stack_grobs <- function(...,
                        grobs = list(...),
                        padding = grid::unit(2, "line"),
                        vp = NULL,
                        gp = NULL,
                        name = NULL) {
  checkmate::assert_true(
    all(vapply(grobs, grid::is.grob, logical(1)))
  )

  if (length(grobs) == 1) {
    return(grobs[[1]])
  }

  n_layout <- 2 * length(grobs) - 1
  hts <- lapply(
    seq(1, n_layout),
    function(i) {
      if (i %% 2 != 0) {
        grid::unit(1, "null")
      } else {
        padding
      }
    }
  )
  hts <- do.call(grid::unit.c, hts)

  main_vp <- grid::viewport(
    layout = grid::grid.layout(nrow = n_layout, ncol = 1, heights = hts)
  )

  nested_grobs <- Map(function(g, i) {
    grid::gTree(
      children = grid::gList(g),
      vp = grid::viewport(layout.pos.row = i, layout.pos.col = 1)
    )
  }, grobs, seq_along(grobs) * 2 - 1)

  grobs_mainvp <- grid::gTree(
    children = do.call(grid::gList, nested_grobs),
    vp = main_vp
  )

  grid::gTree(
    children = grid::gList(grobs_mainvp),
    vp = vp,
    gp = gp,
    name = name
  )
}

#' Arrange Multiple Grobs
#'
#' Arrange grobs as a new grob with \verb{n*m (rows*cols)} layout.
#'
#' @inheritParams stack_grobs
#' @param ncol number of columns in layout.
#' @param nrow number of rows in layout.
#' @param padding_ht unit of length 1, vertical space between each grob.
#' @param padding_wt unit of length 1, horizontal space between each grob.
#'
#' @return A `grob`.
#' @examples
#' library(grid)
#'
#' \donttest{
#' num <- lapply(1:9, textGrob)
#' grid::grid.newpage()
#' grid.draw(arrange_grobs(grobs = num, ncol = 2))
#'
#' showViewport()
#'
#' g1 <- circleGrob(gp = gpar(col = "blue"))
#' g2 <- circleGrob(gp = gpar(col = "red"))
#' g3 <- textGrob("TEST TEXT")
#' grid::grid.newpage()
#' grid.draw(arrange_grobs(g1, g2, g3, nrow = 2))
#'
#' showViewport()
#'
#' grid::grid.newpage()
#' grid.draw(arrange_grobs(g1, g2, g3, ncol = 3))
#'
#' grid::grid.newpage()
#' grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2)))
#' vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2)
#' grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1))
#'
#' showViewport()
#' }
#' @export
arrange_grobs <- function(...,
                          grobs = list(...),
                          ncol = NULL, nrow = NULL,
                          padding_ht = grid::unit(2, "line"),
                          padding_wt = grid::unit(2, "line"),
                          vp = NULL,
                          gp = NULL,
                          name = NULL) {
  checkmate::assert_true(
    all(vapply(grobs, grid::is.grob, logical(1)))
  )

  if (length(grobs) == 1) {
    return(grobs[[1]])
  }

  if (is.null(ncol) && is.null(nrow)) {
    ncol <- 1
    nrow <- ceiling(length(grobs) / ncol)
  } else if (!is.null(ncol) && is.null(nrow)) {
    nrow <- ceiling(length(grobs) / ncol)
  } else if (is.null(ncol) && !is.null(nrow)) {
    ncol <- ceiling(length(grobs) / nrow)
  }

  if (ncol * nrow < length(grobs)) {
    stop("specififed ncol and nrow are not enough for arranging the grobs ")
  }

  if (ncol == 1) {
    return(stack_grobs(grobs = grobs, padding = padding_ht, vp = vp, gp = gp, name = name))
  }

  n_col <- 2 * ncol - 1
  n_row <- 2 * nrow - 1
  hts <- lapply(
    seq(1, n_row),
    function(i) {
      if (i %% 2 != 0) {
        grid::unit(1, "null")
      } else {
        padding_ht
      }
    }
  )
  hts <- do.call(grid::unit.c, hts)

  wts <- lapply(
    seq(1, n_col),
    function(i) {
      if (i %% 2 != 0) {
        grid::unit(1, "null")
      } else {
        padding_wt
      }
    }
  )
  wts <- do.call(grid::unit.c, wts)

  main_vp <- grid::viewport(
    layout = grid::grid.layout(nrow = n_row, ncol = n_col, widths = wts, heights = hts)
  )

  nested_grobs <- list()
  k <- 0
  for (i in seq(nrow) * 2 - 1) {
    for (j in seq(ncol) * 2 - 1) {
      k <- k + 1
      if (k <= length(grobs)) {
        nested_grobs <- c(
          nested_grobs,
          list(grid::gTree(
            children = grid::gList(grobs[[k]]),
            vp = grid::viewport(layout.pos.row = i, layout.pos.col = j)
          ))
        )
      }
    }
  }
  grobs_mainvp <- grid::gTree(
    children = do.call(grid::gList, nested_grobs),
    vp = main_vp
  )

  grid::gTree(
    children = grid::gList(grobs_mainvp),
    vp = vp,
    gp = gp,
    name = name
  )
}

#' Draw `grob`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Draw grob on device page.
#'
#' @param grob grid object
#' @param newpage draw on a new page
#' @param vp a [viewport()] object (or `NULL`).
#'
#' @return A `grob`.
#'
#' @examples
#' library(dplyr)
#' library(grid)
#'
#' \donttest{
#' rect <- rectGrob(width = grid::unit(0.5, "npc"), height = grid::unit(0.5, "npc"))
#' rect %>% draw_grob(vp = grid::viewport(angle = 45))
#'
#' num <- lapply(1:10, textGrob)
#' num %>%
#'   arrange_grobs(grobs = .) %>%
#'   draw_grob()
#' showViewport()
#' }
#'
#' @export
draw_grob <- function(grob, newpage = TRUE, vp = NULL) {
  if (newpage) {
    grid::grid.newpage()
  }
  if (!is.null(vp)) {
    grid::pushViewport(vp)
  }
  grid::grid.draw(grob)
}

tern_grob <- function(x) {
  class(x) <- unique(c("ternGrob", class(x)))
  x
}

#' @keywords internal
print.ternGrob <- function(x, ...) {
  grid::grid.newpage()
  grid::grid.draw(x)
}

#' Counting Patients and Events in Columns
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Counting the number of unique patients and the total number of all and specific events
#' when a column table layout is required.
#'
#' @inheritParams argument_convention
#' @param filters_list (named `list` of `character`)\cr each element in this list describes one
#'   type of event describe by filters, in the same format as [s_count_patients_with_event()].
#'   If it has a label, then this will be used for the column title.
#' @param empty_stats (`character`)\cr optional names of the statistics that should be returned empty such
#'   that corresponding table cells will stay blank.
#' @param custom_label (`string` or `NULL`)\cr if provided and `labelstr` is empty then this will
#'   be used as label.
#' @param .stats (`character`)\cr statistics to select for the table. Run
#'   `get_stats("summarize_patients_events_in_cols")` to see available statistics for this function, in addition
#'   to any added using `filters_list`.
#'
#' @name count_patients_events_in_cols
#' @order 1
NULL

#' @describeIn count_patients_events_in_cols Statistics function which counts numbers of patients and multiple
#'   events defined by filters. Used as analysis function `afun` in `summarize_patients_events_in_cols()`.
#'
#' @return
#' * `s_count_patients_and_multiple_events()` returns a list with the statistics:
#'   - `unique`: number of unique patients in `df`.
#'   - `all`: number of rows in `df`.
#'   - one element with the same name as in `filters_list`: number of rows in `df`,
#'     i.e. events, fulfilling the filter condition.
#'
#' @keywords internal
s_count_patients_and_multiple_events <- function(df, # nolint
                                                 id,
                                                 filters_list,
                                                 empty_stats = character(),
                                                 labelstr = "",
                                                 custom_label = NULL) {
  checkmate::assert_list(filters_list, names = "named")
  checkmate::assert_data_frame(df)
  checkmate::assert_string(id)
  checkmate::assert_disjunct(c("unique", "all"), names(filters_list))
  checkmate::assert_character(empty_stats)
  checkmate::assert_string(labelstr)
  checkmate::assert_string(custom_label, null.ok = TRUE)

  # Below we want to count each row in `df` once, therefore introducing this helper index column.
  df$.row_index <- as.character(seq_len(nrow(df)))
  y <- list()
  row_label <- if (labelstr != "") {
    labelstr
  } else if (!is.null(custom_label)) {
    custom_label
  } else {
    "counts"
  }
  y$unique <- formatters::with_label(
    s_num_patients_content(df = df, .N_col = 1, .var = id, required = NULL)$unique[1L],
    row_label
  )
  y$all <- formatters::with_label(
    nrow(df),
    row_label
  )
  events <- Map(
    function(filters) {
      formatters::with_label(
        s_count_patients_with_event(df = df, .var = ".row_index", filters = filters, .N_col = 1, .N_row = 1)$count,
        row_label
      )
    },
    filters = filters_list
  )
  y_complete <- c(y, events)
  y <- if (length(empty_stats) > 0) {
    y_reduced <- y_complete
    for (stat in intersect(names(y_complete), empty_stats)) {
      y_reduced[[stat]] <- formatters::with_label(character(), obj_label(y_reduced[[stat]]))
    }
    y_reduced
  } else {
    y_complete
  }
  y
}

#' @describeIn count_patients_events_in_cols Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @param col_split (`flag`)\cr whether the columns should be split.
#'   Set to `FALSE` when the required column split has been done already earlier in the layout pipe.
#'
#' @return
#' * `summarize_patients_events_in_cols()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
#'   containing the statistics from `s_count_patients_and_multiple_events()` to the table layout.
#'
#' @examples
#' df <- data.frame(
#'   USUBJID = rep(c("id1", "id2", "id3", "id4"), c(2, 3, 1, 1)),
#'   ARM = c("A", "A", "B", "B", "B", "B", "A"),
#'   AESER = rep("Y", 7),
#'   AESDTH = c("Y", "Y", "N", "Y", "Y", "N", "N"),
#'   AEREL = c("Y", "Y", "N", "Y", "Y", "N", "Y"),
#'   AEDECOD = c("A", "A", "A", "B", "B", "C", "D"),
#'   AEBODSYS = rep(c("SOC1", "SOC2", "SOC3"), c(3, 3, 1))
#' )
#'
#' # `summarize_patients_events_in_cols()`
#' basic_table() %>%
#'   summarize_patients_events_in_cols(
#'     filters_list = list(
#'       related = formatters::with_label(c(AEREL = "Y"), "Events (Related)"),
#'       fatal = c(AESDTH = "Y"),
#'       fatal_related = c(AEREL = "Y", AESDTH = "Y")
#'     ),
#'     custom_label = "%s Total number of patients and events"
#'   ) %>%
#'   build_table(df)
#'
#' @export
#' @order 2
summarize_patients_events_in_cols <- function(lyt, # nolint
                                              id = "USUBJID",
                                              filters_list = list(),
                                              empty_stats = character(),
                                              na_str = default_na_str(),
                                              ...,
                                              .stats = c(
                                                "unique",
                                                "all",
                                                names(filters_list)
                                              ),
                                              .labels = c(
                                                unique = "Patients (All)",
                                                all = "Events (All)",
                                                labels_or_names(filters_list)
                                              ),
                                              col_split = TRUE) {
  extra_args <- list(id = id, filters_list = filters_list, empty_stats = empty_stats, ...)

  afun_list <- Map(
    function(stat) {
      make_afun(
        s_count_patients_and_multiple_events,
        .stats = stat,
        .formats = "xx."
      )
    },
    stat = .stats
  )
  if (col_split) {
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = rep(id, length(.stats)),
      varlabels = .labels[.stats]
    )
  }
  summarize_row_groups(
    lyt = lyt,
    cfun = afun_list,
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Count the Number of Patients with Particular Flags
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The primary analysis variable `.var` denotes the unique patient identifier.
#'
#' @inheritParams argument_convention
#' @param flag_variables (`character`)\cr a character vector specifying the names of `logical`
#'   variables from analysis dataset used for counting the number of unique identifiers.
#' @param flag_labels (`character`)\cr vector of labels to use for flag variables.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_patients_with_flags")`
#'   to see available statistics for this function.
#'
#' @seealso [count_patients_with_event]
#'
#' @name count_patients_with_flags
#' @order 1
NULL

#' @describeIn count_patients_with_flags Statistics function which counts the number of patients for which
#'   a particular flag variable is `TRUE`.
#'
#' @inheritParams analyze_variables
#' @param .var (`character`)\cr name of the column that contains the unique identifier.
#'
#' @note If `flag_labels` is not specified, variables labels will be extracted from `df`. If variables are not
#'   labeled, variable names will be used instead. Alternatively, a named `vector` can be supplied to
#'   `flag_variables` such that within each name-value pair the name corresponds to the variable name and the value is
#'   the label to use for this variable.
#'
#' @return
#' * `s_count_patients_with_flags()` returns the count and the fraction of unique identifiers with each particular
#'   flag as a list of statistics `n`, `count`, `count_fraction`, and `n_blq`, with one element per flag.
#'
#' @examples
#' # `s_count_patients_with_flags()`
#'
#' s_count_patients_with_flags(
#'   adae,
#'   "SUBJID",
#'   flag_variables = c("fl1", "fl2", "fl3", "fl4"),
#'   denom = "N_col",
#'   .N_col = 1000
#' )
#'
#' @export
s_count_patients_with_flags <- function(df,
                                        .var,
                                        flag_variables,
                                        flag_labels = NULL,
                                        .N_col, # nolint
                                        .N_row, # nolint
                                        denom = c("n", "N_row", "N_col")) {
  checkmate::assert_character(flag_variables)
  if (!is.null(flag_labels)) {
    checkmate::assert_character(flag_labels, len = length(flag_variables), any.missing = FALSE)
    flag_names <- flag_labels
  } else {
    if (is.null(names(flag_variables))) {
      flag_names <- formatters::var_labels(df[flag_variables], fill = TRUE)
    } else {
      flag_names <- unname(flag_variables)
      flag_variables <- names(flag_variables)
    }
  }

  checkmate::assert_subset(flag_variables, colnames(df))
  temp <- sapply(flag_variables, function(x) {
    tmp <- Map(function(y) which(df[[y]]), x)
    position_satisfy_flags <- Reduce(intersect, tmp)
    id_satisfy_flags <- as.character(unique(df[position_satisfy_flags, ][[.var]]))
    s_count_values(
      as.character(unique(df[[.var]])),
      id_satisfy_flags,
      denom = denom,
      .N_col = .N_col,
      .N_row = .N_row
    )
  })
  colnames(temp) <- flag_names
  temp <- data.frame(t(temp))
  result <- temp %>% as.list()
  if (length(flag_variables) == 1) {
    for (i in 1:3) names(result[[i]]) <- flag_names[1]
  }
  result
}

#' @describeIn count_patients_with_flags Formatted analysis function which is used as `afun`
#'   in `count_patients_with_flags()`.
#'
#' @return
#' * `a_count_patients_with_flags()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' #  We need to ungroup `count_fraction` first so that the `rtables` formatting
#' # function `format_count_fraction()` can be applied correctly.
#'
#' # `a_count_patients_with_flags()`
#'
#' afun <- make_afun(a_count_patients_with_flags,
#'   .stats = "count_fraction",
#'   .ungroup_stats = "count_fraction"
#' )
#' afun(
#'   adae,
#'   .N_col = 10L,
#'   .N_row = 10L,
#'   .var = "USUBJID",
#'   flag_variables = c("fl1", "fl2", "fl3", "fl4")
#' )
#'
#' @export
a_count_patients_with_flags <- make_afun(
  s_count_patients_with_flags,
  .formats = c("count_fraction" = format_count_fraction_fixed_dp)
)

#' @describeIn count_patients_with_flags Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_patients_with_flags()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_patients_with_flags()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' # Add labelled flag variables to analysis dataset.
#' adae <- tern_ex_adae %>%
#'   mutate(
#'     fl1 = TRUE %>% with_label("Total AEs"),
#'     fl2 = (TRTEMFL == "Y") %>%
#'       with_label("Total number of patients with at least one adverse event"),
#'     fl3 = (TRTEMFL == "Y" & AEOUT == "FATAL") %>%
#'       with_label("Total number of patients with fatal AEs"),
#'     fl4 = (TRTEMFL == "Y" & AEOUT == "FATAL" & AEREL == "Y") %>%
#'       with_label("Total number of patients with related fatal AEs")
#'   )
#'
#' # `count_patients_with_flags()`
#'
#' lyt2 <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_patients_with_flags(
#'     "SUBJID",
#'     flag_variables = c("fl1", "fl2", "fl3", "fl4"),
#'     denom = "N_col"
#'   )
#'
#' build_table(lyt2, adae, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_patients_with_flags <- function(lyt,
                                      var,
                                      flag_variables,
                                      flag_labels = NULL,
                                      var_labels = var,
                                      show_labels = "hidden",
                                      riskdiff = FALSE,
                                      na_str = default_na_str(),
                                      nested = TRUE,
                                      ...,
                                      table_names = paste0("tbl_flags_", var),
                                      .stats = "count_fraction",
                                      .formats = NULL,
                                      .indent_mods = NULL) {
  checkmate::assert_flag(riskdiff)

  s_args <- list(flag_variables = flag_variables, flag_labels = flag_labels, ...)

  afun <- make_afun(
    a_count_patients_with_flags,
    .stats = .stats,
    .formats = .formats,
    .indent_mods = .indent_mods,
    .ungroup_stats = .stats
  )

  extra_args <- if (isFALSE(riskdiff)) {
    s_args
  } else {
    list(
      afun = list("s_count_patients_with_flags" = afun),
      .stats = .stats,
      .indent_mods = .indent_mods,
      s_args = s_args
    )
  }

  lyt <- analyze(
    lyt = lyt,
    vars = var,
    var_labels = var_labels,
    show_labels = show_labels,
    afun = ifelse(isFALSE(riskdiff), afun, afun_riskdiff),
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )

  lyt
}

#' Apply 1/3 or 1/2 Imputation Rule to Data
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @param x_stats (`named list`)\cr a named list of statistics, typically the results of [s_summary()].
#' @param stat (`character`)\cr statistic to return the value/NA level of according to the imputation
#'   rule applied.
#' @param imp_rule (`character`)\cr imputation rule setting. Set to `"1/3"` to implement 1/3 imputation
#'   rule or `"1/2"` to implement 1/2 imputation rule.
#' @param post (`flag`)\cr whether the data corresponds to a post-dose time-point (defaults to `FALSE`).
#'   This parameter is only used when `imp_rule` is set to `"1/3"`.
#' @param avalcat_var (`character`)\cr name of variable that indicates whether a row in `df` corresponds
#'   to an analysis value in category `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or none of the above
#'   (defaults to `"AVALCAT1"`). Variable `avalcat_var` must be present in `df`.
#'
#' @return A `list` containing statistic value (`val`) and NA level (`na_str`) that should be displayed
#'   according to the specified imputation rule.
#'
#' @seealso [analyze_vars_in_cols()] where this function can be implemented by setting the `imp_rule`
#'   argument.
#'
#' @examples
#' set.seed(1)
#' df <- data.frame(
#'   AVAL = runif(50, 0, 1),
#'   AVALCAT1 = sample(c(1, "BLQ"), 50, replace = TRUE)
#' )
#' x_stats <- s_summary(df$AVAL)
#' imputation_rule(df, x_stats, "max", "1/3")
#' imputation_rule(df, x_stats, "geom_mean", "1/3")
#' imputation_rule(df, x_stats, "mean", "1/2")
#'
#' @export
imputation_rule <- function(df, x_stats, stat, imp_rule, post = FALSE, avalcat_var = "AVALCAT1") {
  checkmate::assert_choice(avalcat_var, names(df))
  checkmate::assert_choice(imp_rule, c("1/3", "1/2"))
  n_blq <- sum(grepl("BLQ|LTR|<[1-9]|<PCLLOQ", df[[avalcat_var]]))
  ltr_blq_ratio <- n_blq / max(1, nrow(df))

  # defaults
  val <- x_stats[[stat]]
  na_str <- "NE"

  if (imp_rule == "1/3") {
    if (!post && stat == "geom_mean") val <- NA # 1/3_pre_LT, 1/3_pre_GT
    if (ltr_blq_ratio > 1 / 3) {
      if (stat != "geom_mean") na_str <- "ND" # 1/3_pre_GT, 1/3_post_GT
      if (!post && !stat %in% c("median", "max")) val <- NA # 1/3_pre_GT
      if (post && !stat %in% c("median", "max", "geom_mean")) val <- NA # 1/3_post_GT
    }
  } else if (imp_rule == "1/2") {
    if (ltr_blq_ratio > 1 / 2 && !stat == "max") {
      val <- NA # 1/2_GT
      na_str <- "ND" # 1/2_GT
    }
  }

  list(val = val, na_str = na_str)
}

#' Counting Patients Summing Exposure Across All Patients in Columns
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Counting the number of patients and summing analysis value (i.e exposure values) across all patients
#' when a column table layout is required.
#'
#' @inheritParams argument_convention
#' @param ex_var (`character`)\cr name of the variable within `df` containing exposure values.
#' @param custom_label (`string` or `NULL`)\cr if provided and `labelstr` is empty then this will be used as label.
#' @param .stats (`character`)\cr statistics to select for the table. Run
#' `get_stats("analyze_patients_exposure_in_cols")` to see available statistics for this function.
#'
#' @name summarize_patients_exposure_in_cols
#' @order 1
NULL

#' @describeIn summarize_patients_exposure_in_cols Statistics function which counts numbers
#'   of patients and the sum of exposure across all patients.
#'
#' @return
#' * `s_count_patients_sum_exposure()` returns a named `list` with the statistics:
#'   * `n_patients`: Number of unique patients in `df`.
#'   * `sum_exposure`: Sum of `ex_var` across all patients in `df`.
#'
#' @keywords internal
s_count_patients_sum_exposure <- function(df,
                                          ex_var = "AVAL",
                                          id = "USUBJID",
                                          labelstr = "",
                                          .stats = c("n_patients", "sum_exposure"),
                                          .N_col, # nolint
                                          custom_label = NULL) {
  assert_df_with_variables(df, list(ex_var = ex_var, id = id))
  checkmate::assert_string(id)
  checkmate::assert_string(labelstr)
  checkmate::assert_string(custom_label, null.ok = TRUE)
  checkmate::assert_numeric(df[[ex_var]])
  checkmate::assert_true(all(.stats %in% c("n_patients", "sum_exposure")))

  row_label <- if (labelstr != "") {
    labelstr
  } else if (!is.null(custom_label)) {
    custom_label
  } else {
    "Total patients numbers/person time"
  }

  y <- list()

  if ("n_patients" %in% .stats) {
    y$n_patients <-
      formatters::with_label(
        s_num_patients_content(
          df = df,
          .N_col = .N_col, # nolint
          .var = id,
          labelstr = ""
        )$unique,
        row_label
      )
  }
  if ("sum_exposure" %in% .stats) {
    y$sum_exposure <- formatters::with_label(sum(df[[ex_var]]), row_label)
  }
  y
}

#' @describeIn summarize_patients_exposure_in_cols Analysis function which is used as `afun` in
#'   [rtables::analyze_colvars()] within `analyze_patients_exposure_in_cols()` and as `cfun` in
#'   [rtables::summarize_row_groups()] within `summarize_patients_exposure_in_cols()`.
#'
#' @return
#' * `a_count_patients_sum_exposure()` returns formatted [rtables::CellValue()].
#'
#' @examples
#' a_count_patients_sum_exposure(
#'   df = df,
#'   var = "SEX",
#'   .N_col = nrow(df),
#'   .stats = "n_patients"
#' )
#'
#' @export
a_count_patients_sum_exposure <- function(df,
                                          var = NULL,
                                          ex_var = "AVAL",
                                          id = "USUBJID",
                                          add_total_level = FALSE,
                                          custom_label = NULL,
                                          labelstr = "",
                                          .N_col, # nolint
                                          .stats,
                                          .formats = list(n_patients = "xx (xx.x%)", sum_exposure = "xx")) {
  checkmate::assert_flag(add_total_level)

  if (!is.null(var)) {
    assert_df_with_variables(df, list(var = var))
    df[[var]] <- as.factor(df[[var]])
  }

  y <- list()
  if (is.null(var)) {
    y[[.stats]] <- list(Total = s_count_patients_sum_exposure(
      df = df,
      ex_var = ex_var,
      id = id,
      labelstr = labelstr,
      .N_col = .N_col,
      .stats = .stats,
      custom_label = custom_label
    )[[.stats]])
  } else {
    for (lvl in levels(df[[var]])) {
      y[[.stats]][[lvl]] <- s_count_patients_sum_exposure(
        df = subset(df, get(var) == lvl),
        ex_var = ex_var,
        id = id,
        labelstr = labelstr,
        .N_col = .N_col,
        .stats = .stats,
        custom_label = lvl
      )[[.stats]]
    }
    if (add_total_level) {
      y[[.stats]][["Total"]] <- s_count_patients_sum_exposure(
        df = df,
        ex_var = ex_var,
        id = id,
        labelstr = labelstr,
        .N_col = .N_col,
        .stats = .stats,
        custom_label = custom_label
      )[[.stats]]
    }
  }

  in_rows(.list = y[[.stats]], .formats = .formats[[.stats]])
}

#' @describeIn summarize_patients_exposure_in_cols Layout-creating function which can take statistics
#'   function arguments and additional format arguments. This function is a wrapper for
#'   [rtables::split_cols_by_multivar()] and [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_patients_exposure_in_cols()` returns a layout object suitable for passing to further
#'   layouting functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will
#'   add formatted content rows, with the statistics from `s_count_patients_sum_exposure()` arranged in
#'   columns, to the table layout.
#'
#' @examples
#' lyt5 <- basic_table() %>%
#'   summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE)
#'
#' result5 <- build_table(lyt5, df = df, alt_counts_df = adsl)
#' result5
#'
#' lyt6 <- basic_table() %>%
#'   summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE, .stats = "sum_exposure")
#'
#' result6 <- build_table(lyt6, df = df, alt_counts_df = adsl)
#' result6
#'
#' @export
#' @order 3
summarize_patients_exposure_in_cols <- function(lyt, # nolint
                                                var,
                                                ex_var = "AVAL",
                                                id = "USUBJID",
                                                add_total_level = FALSE,
                                                custom_label = NULL,
                                                col_split = TRUE,
                                                na_str = default_na_str(),
                                                ...,
                                                .stats = c("n_patients", "sum_exposure"),
                                                .labels = c(n_patients = "Patients", sum_exposure = "Person time"),
                                                .indent_mods = NULL) {
  extra_args <- list(ex_var = ex_var, id = id, add_total_level = add_total_level, custom_label = custom_label, ...)

  if (col_split) {
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = rep(var, length(.stats)),
      varlabels = .labels[.stats],
      extra_args = list(.stats = .stats)
    )
  }
  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = a_count_patients_sum_exposure,
    na_str = na_str,
    extra_args = extra_args
  )
}

#' @describeIn summarize_patients_exposure_in_cols Layout-creating function which can take statistics
#'   function arguments and additional format arguments. This function is a wrapper for
#'   [rtables::split_cols_by_multivar()] and [rtables::analyze_colvars()].
#'
#' @param col_split (`flag`)\cr whether the columns should be split. Set to `FALSE` when the required
#'   column split has been done already earlier in the layout pipe.
#'
#' @return
#' * `analyze_patients_exposure_in_cols()` returns a layout object suitable for passing to further
#'   layouting functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will
#'   add formatted data rows, with the statistics from `s_count_patients_sum_exposure()` arranged in
#'   columns, to the table layout.
#'
#' @note As opposed to [summarize_patients_exposure_in_cols()] which generates content rows,
#'   `analyze_patients_exposure_in_cols()` generates data rows which will _not_ be repeated on multiple
#'   pages when pagination is used.
#'
#' @examples
#' set.seed(1)
#' df <- data.frame(
#'   USUBJID = c(paste("id", seq(1, 12), sep = "")),
#'   ARMCD = c(rep("ARM A", 6), rep("ARM B", 6)),
#'   SEX = c(rep("Female", 6), rep("Male", 6)),
#'   AVAL = as.numeric(sample(seq(1, 20), 12)),
#'   stringsAsFactors = TRUE
#' )
#' adsl <- data.frame(
#'   USUBJID = c(paste("id", seq(1, 12), sep = "")),
#'   ARMCD = c(rep("ARM A", 2), rep("ARM B", 2)),
#'   SEX = c(rep("Female", 2), rep("Male", 2)),
#'   stringsAsFactors = TRUE
#' )
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
#'   summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE) %>%
#'   analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE)
#' result <- build_table(lyt, df = df, alt_counts_df = adsl)
#' result
#'
#' lyt2 <- basic_table() %>%
#'   split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
#'   summarize_patients_exposure_in_cols(
#'     var = "AVAL", col_split = TRUE,
#'     .stats = "n_patients", custom_label = "some custom label"
#'   ) %>%
#'   analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE, ex_var = "AVAL")
#' result2 <- build_table(lyt2, df = df, alt_counts_df = adsl)
#' result2
#'
#' lyt3 <- basic_table() %>%
#'   analyze_patients_exposure_in_cols(var = "SEX", col_split = TRUE, ex_var = "AVAL")
#' result3 <- build_table(lyt3, df = df, alt_counts_df = adsl)
#' result3
#'
#' # Adding total levels and custom label
#' lyt4 <- basic_table(
#'   show_colcounts = TRUE
#' ) %>%
#'   analyze_patients_exposure_in_cols(
#'     var = "ARMCD",
#'     col_split = TRUE,
#'     add_total_level = TRUE,
#'     custom_label = "TOTAL"
#'   ) %>%
#'   append_topleft(c("", "Sex"))
#'
#' result4 <- build_table(lyt4, df = df, alt_counts_df = adsl)
#' result4
#'
#' @export
#' @order 2
analyze_patients_exposure_in_cols <- function(lyt, # nolint
                                              var = NULL,
                                              ex_var = "AVAL",
                                              id = "USUBJID",
                                              add_total_level = FALSE,
                                              custom_label = NULL,
                                              col_split = TRUE,
                                              na_str = default_na_str(),
                                              .stats = c("n_patients", "sum_exposure"),
                                              .labels = c(n_patients = "Patients", sum_exposure = "Person time"),
                                              .indent_mods = 0L,
                                              ...) {
  extra_args <- list(
    var = var, ex_var = ex_var, id = id, add_total_level = add_total_level, custom_label = custom_label, ...
  )

  if (col_split) {
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = rep(ex_var, length(.stats)),
      varlabels = .labels[.stats],
      extra_args = list(.stats = .stats)
    )
  }
  lyt <- lyt %>% analyze_colvars(
    afun = a_count_patients_sum_exposure,
    indent_mod = .indent_mods,
    na_str = na_str,
    extra_args = extra_args
  )
  lyt
}

#' Combination Functions Class
#'
#' @description `r lifecycle::badge("stable")`
#'
#' `CombinationFunction` is an S4 class which extends standard functions. These are special functions that
#' can be combined and negated with the logical operators.
#'
#' @param e1 (`CombinationFunction`)\cr left hand side of logical operator.
#' @param e2 (`CombinationFunction`)\cr right hand side of logical operator.
#' @param x (`CombinationFunction`)\cr the function which should be negated.
#'
#' @return Returns a logical value indicating whether the left hand side of the equation equals the right hand side.
#'
#' @exportClass CombinationFunction
#' @export CombinationFunction
#'
#' @examples
#' higher <- function(a) {
#'   force(a)
#'   CombinationFunction(
#'     function(x) {
#'       x > a
#'     }
#'   )
#' }
#'
#' lower <- function(b) {
#'   force(b)
#'   CombinationFunction(
#'     function(x) {
#'       x < b
#'     }
#'   )
#' }
#'
#' c1 <- higher(5)
#' c2 <- lower(10)
#' c3 <- higher(5) & lower(10)
#' c3(7)
#'
#' @aliases CombinationFunction-class
#' @name combination_function
CombinationFunction <- methods::setClass("CombinationFunction", contains = "function") # nolint

#' @describeIn combination_function Logical "AND" combination of `CombinationFunction` functions.
#'   The resulting object is of the same class, and evaluates the two argument functions. The result
#'   is then the "AND" of the two individual results.
#'
#' @export
methods::setMethod(
  "&",
  signature = c(e1 = "CombinationFunction", e2 = "CombinationFunction"),
  definition = function(e1, e2) {
    CombinationFunction(function(...) {
      e1(...) && e2(...)
    })
  }
)

#' @describeIn combination_function Logical "OR" combination of `CombinationFunction` functions.
#'   The resulting object is of the same class, and evaluates the two argument functions. The result
#'   is then the "OR" of the two individual results.
#'
#' @export
methods::setMethod(
  "|",
  signature = c(e1 = "CombinationFunction", e2 = "CombinationFunction"),
  definition = function(e1, e2) {
    CombinationFunction(function(...) {
      e1(...) || e2(...)
    })
  }
)

#' @describeIn combination_function Logical negation of `CombinationFunction` functions.
#'   The resulting object is of the same class, and evaluates the original function. The result
#'   is then the opposite of this results.
#'
#' @export
methods::setMethod(
  "!",
  signature = c(x = "CombinationFunction"),
  definition = function(x) {
    CombinationFunction(function(...) {
      !x(...)
    })
  }
)

#' Patient Counts with Abnormal Range Values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Primary analysis variable `.var` indicates the abnormal range result (`character` or `factor`)
#' and additional analysis variables are `id` (`character` or `factor`) and `baseline` (`character` or
#' `factor`). For each direction specified in `abnormal` (e.g. high or low) count patients in the
#' numerator and denominator as follows:
#'   * `num` : The number of patients with this abnormality recorded while on treatment.
#'   * `denom`: The number of patients with at least one post-baseline assessment.
#'
#' @inheritParams argument_convention
#' @param abnormal (named `list`)\cr list identifying the abnormal range level(s) in `var`. Defaults to
#'   `list(Low = "LOW", High = "HIGH")` but you can also group different levels into the named list,
#'   for example, `abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH"))`.
#' @param exclude_base_abn (`flag`)\cr whether to exclude subjects with baseline abnormality
#'   from numerator and denominator.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("abnormal")`
#'   to see available statistics for this function.
#'
#' @note
#' * `count_abnormal()` only works with a single variable containing multiple abnormal levels.
#' * `df` should be filtered to include only post-baseline records.
#' * the denominator includes patients that might have other abnormal levels at baseline,
#'   and patients with missing baseline. Patients with these abnormalities at
#'   baseline can be optionally excluded from numerator and denominator.
#'
#' @name abnormal
#' @include formatting_functions.R
#' @order 1
NULL

#' @describeIn abnormal Statistics function which counts patients with abnormal range values
#'   for a single `abnormal` level.
#'
#' @return
#' * `s_count_abnormal()` returns the statistic `fraction` which is a vector with `num` and `denom` counts of patients.
#'
#' @keywords internal
s_count_abnormal <- function(df,
                             .var,
                             abnormal = list(Low = "LOW", High = "HIGH"),
                             variables = list(id = "USUBJID", baseline = "BNRIND"),
                             exclude_base_abn = FALSE) {
  checkmate::assert_list(abnormal, types = "character", names = "named", len = 2, any.missing = FALSE)
  checkmate::assert_true(any(unlist(abnormal) %in% levels(df[[.var]])))
  checkmate::assert_factor(df[[.var]])
  checkmate::assert_flag(exclude_base_abn)
  assert_df_with_variables(df, c(range = .var, variables))
  checkmate::assert_multi_class(df[[variables$baseline]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))

  count_abnormal_single <- function(abn_name, abn) {
    # Patients in the denominator fulfill:
    # - have at least one post-baseline visit
    # - their baseline must not be abnormal if `exclude_base_abn`.
    if (exclude_base_abn) {
      denom_select <- !(df[[variables$baseline]] %in% abn)
    } else {
      denom_select <- TRUE
    }
    denom <- length(unique(df[denom_select, variables$id, drop = TRUE]))

    # Patients in the numerator fulfill:
    # - have at least one post-baseline visit with the required abnormality level
    # - are part of the denominator patients.
    num_select <- (df[[.var]] %in% abn) & denom_select
    num <- length(unique(df[num_select, variables$id, drop = TRUE]))

    formatters::with_label(c(num = num, denom = denom), abn_name)
  }

  # This will define the abnormal levels theoretically possible for a specific lab parameter
  # within a split level of a layout.
  abnormal_lev <- lapply(abnormal, intersect, levels(df[[.var]]))
  abnormal_lev <- abnormal_lev[vapply(abnormal_lev, function(x) length(x) > 0, logical(1))]

  result <- sapply(names(abnormal_lev), function(i) count_abnormal_single(i, abnormal_lev[[i]]), simplify = FALSE)
  result <- list(fraction = result)
  result
}

#' @describeIn abnormal Formatted analysis function which is used as `afun` in `count_abnormal()`.
#'
#' @return
#' * `a_count_abnormal()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal <- make_afun(
  s_count_abnormal,
  .formats = c(fraction = format_fraction)
)

#' @describeIn abnormal Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(1, 1, 2, 2)),
#'   ANRIND = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
#'   BNRIND = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
#'   ONTRTFL = c("", "Y", "", "Y"),
#'   stringsAsFactors = FALSE
#' )
#'
#' # Select only post-baseline records.
#' df <- df %>%
#'   filter(ONTRTFL == "Y")
#'
#' # Layout creating function.
#' basic_table() %>%
#'   count_abnormal(var = "ANRIND", abnormal = list(high = "HIGH", low = "LOW")) %>%
#'   build_table(df)
#'
#' # Passing of statistics function and formatting arguments.
#' df2 <- data.frame(
#'   ID = as.character(c(1, 1, 2, 2)),
#'   RANGE = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
#'   BL_RANGE = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
#'   ONTRTFL = c("", "Y", "", "Y"),
#'   stringsAsFactors = FALSE
#' )
#'
#' # Select only post-baseline records.
#' df2 <- df2 %>%
#'   filter(ONTRTFL == "Y")
#'
#' basic_table() %>%
#'   count_abnormal(
#'     var = "RANGE",
#'     abnormal = list(low = "LOW", high = "HIGH"),
#'     variables = list(id = "ID", baseline = "BL_RANGE")
#'   ) %>%
#'   build_table(df2)
#'
#' @export
#' @order 2
count_abnormal <- function(lyt,
                           var,
                           abnormal = list(Low = "LOW", High = "HIGH"),
                           variables = list(id = "USUBJID", baseline = "BNRIND"),
                           exclude_base_abn = FALSE,
                           na_str = default_na_str(),
                           nested = TRUE,
                           ...,
                           table_names = var,
                           .stats = NULL,
                           .formats = NULL,
                           .labels = NULL,
                           .indent_mods = NULL) {
  extra_args <- list(abnormal = abnormal, variables = variables, exclude_base_abn = exclude_base_abn, ...)

  afun <- make_afun(
    a_count_abnormal,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "fraction"
  )

  checkmate::assert_string(var)

  analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    table_names = table_names,
    extra_args = extra_args,
    show_labels = "hidden"
  )
}

#' Counting Specific Values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' We can count the occurrence of specific values in a variable of interest.
#'
#' @inheritParams argument_convention
#' @param values (`character`)\cr specific values that should be counted.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_values")`
#'   to see available statistics for this function.
#'
#' @note
#' * For `factor` variables, `s_count_values` checks whether `values` are all included in the levels of `x`
#'   and fails otherwise.
#' * For `count_values()`, variable labels are shown when there is more than one element in `vars`,
#'   otherwise they are hidden.
#'
#' @name count_values_funs
#' @order 1
NULL

#' @describeIn count_values_funs S3 generic function to count values.
#'
#' @inheritParams s_summary.logical
#'
#' @return
#' * `s_count_values()` returns output of [s_summary()] for specified values of a non-numeric variable.
#'
#' @export
s_count_values <- function(x,
                           values,
                           na.rm = TRUE, # nolint
                           .N_col, # nolint
                           .N_row, # nolint
                           denom = c("n", "N_row", "N_col")) {
  UseMethod("s_count_values", x)
}

#' @describeIn count_values_funs Method for `character` class.
#'
#' @method s_count_values character
#'
#' @examples
#' # `s_count_values.character`
#' s_count_values(x = c("a", "b", "a"), values = "a")
#' s_count_values(x = c("a", "b", "a", NA, NA), values = "b", na.rm = FALSE)
#'
#' @export
s_count_values.character <- function(x,
                                     values = "Y",
                                     na.rm = TRUE, # nolint
                                     ...) {
  checkmate::assert_character(values)

  if (na.rm) {
    x <- x[!is.na(x)]
  }

  is_in_values <- x %in% values

  s_summary(is_in_values, ...)
}

#' @describeIn count_values_funs Method for `factor` class. This makes an automatic
#'   conversion to `character` and then forwards to the method for characters.
#'
#' @method s_count_values factor
#'
#' @examples
#' # `s_count_values.factor`
#' s_count_values(x = factor(c("a", "b", "a")), values = "a")
#'
#' @export
s_count_values.factor <- function(x,
                                  values = "Y",
                                  ...) {
  s_count_values(as.character(x), values = as.character(values), ...)
}

#' @describeIn count_values_funs Method for `logical` class.
#'
#' @method s_count_values logical
#'
#' @examples
#' # `s_count_values.logical`
#' s_count_values(x = c(TRUE, FALSE, TRUE))
#'
#' @export
s_count_values.logical <- function(x, values = TRUE, ...) {
  checkmate::assert_logical(values)
  s_count_values(as.character(x), values = as.character(values), ...)
}

#' @describeIn count_values_funs Formatted analysis function which is used as `afun`
#'   in `count_values()`.
#'
#' @return
#' * `a_count_values()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' # `a_count_values`
#' a_count_values(x = factor(c("a", "b", "a")), values = "a", .N_col = 10, .N_row = 10)
#'
#' @export
a_count_values <- make_afun(
  s_count_values,
  .formats = c(count_fraction = "xx (xx.xx%)", count = "xx")
)

#' @describeIn count_values_funs Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_values()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_values()` to the table layout.
#'
#' @examples
#' # `count_values`
#' basic_table() %>%
#'   count_values("Species", values = "setosa") %>%
#'   build_table(iris)
#'
#' @export
#' @order 2
count_values <- function(lyt,
                         vars,
                         values,
                         na_str = default_na_str(),
                         nested = TRUE,
                         ...,
                         table_names = vars,
                         .stats = "count_fraction",
                         .formats = NULL,
                         .labels = c(count_fraction = paste(values, collapse = ", ")),
                         .indent_mods = NULL) {
  extra_args <- list(values = values, ...)

  afun <- make_afun(
    a_count_values,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  analyze(
    lyt,
    vars,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = ifelse(length(vars) > 1, "visible", "hidden"),
    table_names = table_names
  )
}

#' Cumulative Counts with Thresholds
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Summarize cumulative counts of a (`numeric`) vector that is less than, less or equal to,
#' greater than, or greater or equal to user-specific thresholds.
#'
#' @inheritParams h_count_cumulative
#' @inheritParams argument_convention
#' @param thresholds (`numeric`)\cr vector of cutoff value for the counts.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_cumulative")`
#'   to see available statistics for this function.
#'
#' @seealso Relevant helper function [h_count_cumulative()], and descriptive function [d_count_cumulative()].
#'
#' @name count_cumulative
#' @order 1
NULL

#' Helper Function for [s_count_cumulative()]
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to calculate count and fraction of `x` values in the lower or upper tail given a threshold.
#'
#' @inheritParams argument_convention
#' @param threshold (`number`)\cr a cutoff value as threshold to count values of `x`.
#' @param lower_tail (`logical`)\cr whether to count lower tail, default is `TRUE`.
#' @param include_eq (`logical`)\cr whether to include value equal to the `threshold` in
#'   count, default is `TRUE`.
#'
#' @return A named vector with items:
#'   * `count`: the count of values less than, less or equal to, greater than, or greater or equal to a threshold
#'     of user specification.
#'   * `fraction`: the fraction of the count.
#'
#' @seealso [count_cumulative]
#'
#' @examples
#' set.seed(1, kind = "Mersenne-Twister")
#' x <- c(sample(1:10, 10), NA)
#' .N_col <- length(x)
#'
#' h_count_cumulative(x, 5, .N_col = .N_col)
#' h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col)
#' h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col)
#' h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col)
#'
#' @export
h_count_cumulative <- function(x,
                               threshold,
                               lower_tail = TRUE,
                               include_eq = TRUE,
                               na.rm = TRUE, # nolint
                               .N_col) { # nolint
  checkmate::assert_numeric(x)
  checkmate::assert_numeric(threshold)
  checkmate::assert_numeric(.N_col)
  checkmate::assert_flag(lower_tail)
  checkmate::assert_flag(include_eq)
  checkmate::assert_flag(na.rm)

  is_keep <- if (na.rm) !is.na(x) else rep(TRUE, length(x))
  count <- if (lower_tail && include_eq) {
    length(x[is_keep & x <= threshold])
  } else if (lower_tail && !include_eq) {
    length(x[is_keep & x < threshold])
  } else if (!lower_tail && include_eq) {
    length(x[is_keep & x >= threshold])
  } else if (!lower_tail && !include_eq) {
    length(x[is_keep & x > threshold])
  }

  result <- c(count = count, fraction = count / .N_col)
  result
}

#' Description of Cumulative Count
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a helper function that describes the analysis in [s_count_cumulative()].
#'
#' @inheritParams h_count_cumulative
#'
#' @return Labels for [s_count_cumulative()].
#'
#' @export
d_count_cumulative <- function(threshold, lower_tail, include_eq) {
  checkmate::assert_numeric(threshold)
  lg <- if (lower_tail) "<" else ">"
  eq <- if (include_eq) "=" else ""
  paste0(lg, eq, " ", threshold)
}

#' @describeIn count_cumulative Statistics function that produces a named list given a numeric vector of thresholds.
#'
#' @return
#' * `s_count_cumulative()` returns a named list of `count_fraction`s: a list with each `thresholds` value as a
#'   component, each component containing a vector for the count and fraction.
#'
#' @keywords internal
s_count_cumulative <- function(x,
                               thresholds,
                               lower_tail = TRUE,
                               include_eq = TRUE,
                               .N_col, # nolint
                               ...) {
  checkmate::assert_numeric(thresholds, min.len = 1, any.missing = FALSE)

  count_fraction_list <- Map(function(thres) {
    result <- h_count_cumulative(x, thres, lower_tail, include_eq, .N_col = .N_col, ...)
    label <- d_count_cumulative(thres, lower_tail, include_eq)
    formatters::with_label(result, label)
  }, thresholds)

  names(count_fraction_list) <- thresholds
  list(count_fraction = count_fraction_list)
}

#' @describeIn count_cumulative Formatted analysis function which is used as `afun`
#'   in `count_cumulative()`.
#'
#' @return
#' * `a_count_cumulative()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_cumulative <- make_afun(
  s_count_cumulative,
  .formats = c(count_fraction = format_count_fraction)
)

#' @describeIn count_cumulative Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_cumulative()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_cumulative()` to the table layout.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_cumulative(
#'     vars = "AGE",
#'     thresholds = c(40, 60)
#'   ) %>%
#'   build_table(tern_ex_adsl)
#'
#' @export
#' @order 2
count_cumulative <- function(lyt,
                             vars,
                             thresholds,
                             lower_tail = TRUE,
                             include_eq = TRUE,
                             var_labels = vars,
                             show_labels = "visible",
                             na_str = default_na_str(),
                             nested = TRUE,
                             ...,
                             table_names = vars,
                             .stats = NULL,
                             .formats = NULL,
                             .labels = NULL,
                             .indent_mods = NULL) {
  extra_args <- list(thresholds = thresholds, lower_tail = lower_tail, include_eq = include_eq, ...)

  afun <- make_afun(
    a_count_cumulative,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )
  analyze(
    lyt,
    vars,
    afun = afun,
    na_str = na_str,
    table_names = table_names,
    var_labels = var_labels,
    show_labels = show_labels,
    nested = nested,
    extra_args = extra_args
  )
}

#' Bland Altman analysis
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Functions of bland altman method to assess the agreement between two numerical vectors.
#'
#' @inheritParams argument_convention
#' @param y ('numeric')\cr vector of numbers we want to analyze, which we want to compare with x.
#'
#' @name bland_altman
#' @examples
#' x <- seq(1, 60, 5)
#' y <- seq(5, 50, 4)
#' conf_level <- 0.9
#' # Derive statistics that are needed for Bland Altman plot
#' s_bland_altman(x, y, conf_level = conf_level)
#' # Create a Bland Altman plot
#' g_bland_altman(x, y, conf_level = conf_level)
NULL

#' @describeIn bland_altman
#'
#' @export
s_bland_altman <- function(x, y, conf_level = 0.95) {
  checkmate::assert_numeric(x, min.len = 1, any.missing = TRUE)
  checkmate::assert_numeric(y, len = length(x), any.missing = TRUE)
  checkmate::assert_numeric(conf_level, lower = 0, upper = 1, any.missing = TRUE)

  alpha <- 1 - conf_level

  ind <- complete.cases(x, y) # use only pairwise complete observations, and check if x and y have the same length
  x <- x[ind]
  y <- y[ind]
  n <- sum(ind) # number of 'observations'

  if (n == 0) {
    stop("there is no valid paired data")
  }

  difference <- x - y # vector of differences
  average <- (x + y) / 2 # vector of means
  difference_mean <- mean(difference) # mean difference
  difference_sd <- sd(difference) # SD of differences
  al <- qnorm(1 - alpha / 2) * difference_sd
  upper_agreement_limit <- difference_mean + al # agreement limits
  lower_agreement_limit <- difference_mean - al


  difference_se <- difference_sd / sqrt(n) # standard error of the mean
  al_se <- difference_sd * sqrt(3) / sqrt(n) # standard error of the agreement limit
  tvalue <- qt(1 - alpha / 2, n - 1) # t value for 95% CI calculation
  difference_mean_ci <- difference_se * tvalue
  al_ci <- al_se * tvalue
  upper_agreement_limit_ci <- c(upper_agreement_limit - al_ci, upper_agreement_limit + al_ci)
  lower_agreement_limit_ci <- c(lower_agreement_limit - al_ci, lower_agreement_limit + al_ci)


  list(
    df = data.frame(average, difference),
    difference_mean = difference_mean,
    ci_mean = difference_mean + c(-1, 1) * difference_mean_ci,
    difference_sd = difference_sd,
    difference_se = difference_se,
    upper_agreement_limit = upper_agreement_limit,
    lower_agreement_limit = lower_agreement_limit,
    agreement_limit_se = al_se,
    upper_agreement_limit_ci = upper_agreement_limit_ci,
    lower_agreement_limit_ci = lower_agreement_limit_ci,
    t_value = tvalue,
    n = n
  )
}

#' @describeIn bland_altman
#'
#' @export
g_bland_altman <- function(x, y, conf_level = 0.95) {
  result_tem <- s_bland_altman(x, y, conf_level = conf_level)
  xpos <- max(result_tem$df$average) * 0.9 + min(result_tem$df$average) * 0.1
  yrange <- diff(range(result_tem$df$difference))

  p <- ggplot(result_tem$df) +
    geom_point(aes(x = average, y = difference), color = "blue") +
    geom_hline(yintercept = result_tem$difference_mean, color = "blue", linetype = 1) +
    geom_hline(yintercept = 0, color = "blue", linetype = 2) +
    geom_hline(yintercept = result_tem$lower_agreement_limit, color = "red", linetype = 2) +
    geom_hline(yintercept = result_tem$upper_agreement_limit, color = "red", linetype = 2) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$lower_agreement_limit + 0.03 * yrange,
      label = "lower limits of agreement",
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$upper_agreement_limit + 0.03 * yrange,
      label = "upper limits of agreement",
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$difference_mean + 0.03 * yrange,
      label = "mean of difference between two measures",
      color = "blue"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$lower_agreement_limit - 0.03 * yrange,
      label = sprintf("%.2f", result_tem$lower_agreement_limit),
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$upper_agreement_limit - 0.03 * yrange,
      label = sprintf("%.2f", result_tem$upper_agreement_limit),
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$difference_mean - 0.03 * yrange,
      label = sprintf("%.2f", result_tem$difference_meanm),
      color = "blue"
    ) +
    xlab("Average of two measures") +
    ylab("Difference between two measures")

  return(p)
}

#' Helper Function for Deriving Analysis Datasets for `LBT13` and `LBT14`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function that merges `ADSL` and `ADLB` datasets so that missing lab test records are inserted in the
#' output dataset. Remember that `na_level` must match the needed pre-processing
#' done with [df_explicit_na()] to have the desired output.
#'
#' @param adsl (`data.frame`)\cr `ADSL` dataframe.
#' @param adlb (`data.frame`)\cr `ADLB` dataframe.
#' @param worst_flag (named `vector`)\cr Worst post-baseline lab flag variable.
#' @param by_visit (`logical`)\cr defaults to `FALSE` to generate worst grade per patient.
#'   If worst grade per patient per visit is specified for `worst_flag`, then
#'   `by_visit` should be `TRUE` to generate worst grade patient per visit.
#' @param no_fillin_visits (named `character`)\cr Visits that are not considered for post-baseline worst toxicity
#'   grade. Defaults to `c("SCREENING", "BASELINE")`.
#'
#' @return `df` containing variables shared between `adlb` and `adsl` along with variables `PARAM`, `PARAMCD`,
#'   `ATOXGR`, and `BTOXGR` relevant for analysis. Optionally, `AVISIT` are `AVISITN` are included when
#'   `by_visit = TRUE` and `no_fillin_visits = c("SCREENING", "BASELINE")`.
#'
#' @details In the result data missing records will be created for the following situations:
#'   * Patients who are present in `adsl` but have no lab data in `adlb` (both baseline and post-baseline).
#'   * Patients who do not have any post-baseline lab values.
#'   * Patients without any post-baseline values flagged as the worst.
#'
#' @examples
#' # `h_adsl_adlb_merge_using_worst_flag`
#' adlb_out <- h_adsl_adlb_merge_using_worst_flag(
#'   tern_ex_adsl,
#'   tern_ex_adlb,
#'   worst_flag = c("WGRHIFL" = "Y")
#' )
#'
#' # `h_adsl_adlb_merge_using_worst_flag` by visit example
#' adlb_out_by_visit <- h_adsl_adlb_merge_using_worst_flag(
#'   tern_ex_adsl,
#'   tern_ex_adlb,
#'   worst_flag = c("WGRLOVFL" = "Y"),
#'   by_visit = TRUE
#' )
#'
#' @export
h_adsl_adlb_merge_using_worst_flag <- function(adsl, # nolint
                                               adlb,
                                               worst_flag = c("WGRHIFL" = "Y"),
                                               by_visit = FALSE,
                                               no_fillin_visits = c("SCREENING", "BASELINE")) {
  col_names <- names(worst_flag)
  filter_values <- worst_flag

  temp <- Map(
    function(x, y) which(adlb[[x]] == y),
    col_names,
    filter_values
  )

  position_satisfy_filters <- Reduce(intersect, temp)

  adsl_adlb_common_columns <- intersect(colnames(adsl), colnames(adlb))
  columns_from_adlb <- c("USUBJID", "PARAM", "PARAMCD", "AVISIT", "AVISITN", "ATOXGR", "BTOXGR")

  adlb_f <- adlb[position_satisfy_filters, ] %>%
    dplyr::filter(!.data[["AVISIT"]] %in% no_fillin_visits)
  adlb_f <- adlb_f[, columns_from_adlb]

  avisits_grid <- adlb %>%
    dplyr::filter(!.data[["AVISIT"]] %in% no_fillin_visits) %>%
    dplyr::pull(.data[["AVISIT"]]) %>%
    unique()

  if (by_visit) {
    adsl_lb <- expand.grid(
      USUBJID = unique(adsl$USUBJID),
      AVISIT = avisits_grid,
      PARAMCD = unique(adlb$PARAMCD)
    )

    adsl_lb <- adsl_lb %>%
      dplyr::left_join(unique(adlb[c("AVISIT", "AVISITN")]), by = "AVISIT") %>%
      dplyr::left_join(unique(adlb[c("PARAM", "PARAMCD")]), by = "PARAMCD")

    adsl1 <- adsl[, adsl_adlb_common_columns]
    adsl_lb <- adsl1 %>% merge(adsl_lb, by = "USUBJID")

    by_variables_from_adlb <- c("USUBJID", "AVISIT", "AVISITN", "PARAMCD", "PARAM")

    adlb_btoxgr <- adlb %>%
      dplyr::select(c("USUBJID", "PARAMCD", "BTOXGR")) %>%
      unique() %>%
      dplyr::rename("BTOXGR_MAP" = "BTOXGR")

    adlb_out <- merge(
      adlb_f,
      adsl_lb,
      by = by_variables_from_adlb,
      all = TRUE,
      sort = FALSE
    )
    adlb_out <- adlb_out %>%
      dplyr::left_join(adlb_btoxgr, by = c("USUBJID", "PARAMCD")) %>%
      dplyr::mutate(BTOXGR = .data$BTOXGR_MAP) %>%
      dplyr::select(-"BTOXGR_MAP")

    adlb_var_labels <- c(
      formatters::var_labels(adlb[by_variables_from_adlb]),
      formatters::var_labels(adlb[columns_from_adlb[!columns_from_adlb %in% by_variables_from_adlb]]),
      formatters::var_labels(adsl[adsl_adlb_common_columns[adsl_adlb_common_columns != "USUBJID"]])
    )
  } else {
    adsl_lb <- expand.grid(
      USUBJID = unique(adsl$USUBJID),
      PARAMCD = unique(adlb$PARAMCD)
    )

    adsl_lb <- adsl_lb %>% dplyr::left_join(unique(adlb[c("PARAM", "PARAMCD")]), by = "PARAMCD")

    adsl1 <- adsl[, adsl_adlb_common_columns]
    adsl_lb <- adsl1 %>% merge(adsl_lb, by = "USUBJID")

    by_variables_from_adlb <- c("USUBJID", "PARAMCD", "PARAM")

    adlb_out <- merge(
      adlb_f,
      adsl_lb,
      by = by_variables_from_adlb,
      all = TRUE,
      sort = FALSE
    )

    adlb_var_labels <- c(
      formatters::var_labels(adlb[by_variables_from_adlb]),
      formatters::var_labels(adlb[columns_from_adlb[!columns_from_adlb %in% by_variables_from_adlb]]),
      formatters::var_labels(adsl[adsl_adlb_common_columns[adsl_adlb_common_columns != "USUBJID"]])
    )
  }

  adlb_out$ATOXGR <- as.factor(adlb_out$ATOXGR)
  adlb_out$BTOXGR <- as.factor(adlb_out$BTOXGR)

  formatters::var_labels(adlb_out) <- adlb_var_labels

  adlb_out
}

#' Count the Number of Patients with a Particular Event
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The primary analysis variable `.var` denotes the unique patient identifier.
#'
#' @inheritParams argument_convention
#' @param filters (`character`)\cr a character vector specifying the column names and flag variables
#'   to be used for counting the number of unique identifiers satisfying such conditions.
#'   Multiple column names and flags are accepted in this format
#'   `c("column_name1" = "flag1", "column_name2" = "flag2")`.
#'   Note that only equality is being accepted as condition.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_patients_with_event")`
#'   to see available statistics for this function.
#'
#' @seealso [count_patients_with_flags]
#'
#' @name count_patients_with_event
#' @order 1
NULL

#' @describeIn count_patients_with_event Statistics function which counts the number of patients for which
#'   the defined event has occurred.
#'
#' @inheritParams analyze_variables
#' @param .var (`character`)\cr name of the column that contains the unique identifier.
#'
#' @return
#' * `s_count_patients_with_event()` returns the count and fraction of unique identifiers with the defined event.
#'
#' @examples
#' # `s_count_patients_with_event()`
#'
#' s_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y")
#' )
#'
#' s_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL")
#' )
#'
#' s_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
#'   denom = "N_col",
#'   .N_col = 456
#' )
#'
#' @export
s_count_patients_with_event <- function(df,
                                        .var,
                                        filters,
                                        .N_col, # nolint
                                        .N_row, # nolint
                                        denom = c("n", "N_row", "N_col")) {
  col_names <- names(filters)
  filter_values <- filters

  checkmate::assert_subset(col_names, colnames(df))

  temp <- Map(
    function(x, y) which(df[[x]] == y),
    col_names,
    filter_values
  )
  position_satisfy_filters <- Reduce(intersect, temp)
  id_satisfy_filters <- as.character(unique(df[position_satisfy_filters, ][[.var]]))
  result <- s_count_values(
    as.character(unique(df[[.var]])),
    id_satisfy_filters,
    denom = denom,
    .N_col = .N_col,
    .N_row = .N_row
  )
  result
}

#' @describeIn count_patients_with_event Formatted analysis function which is used as `afun`
#'   in `count_patients_with_event()`.
#'
#' @return
#' * `a_count_patients_with_event()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' # `a_count_patients_with_event()`
#'
#' a_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y"),
#'   .N_col = 100,
#'   .N_row = 100
#' )
#'
#' @export
a_count_patients_with_event <- make_afun(
  s_count_patients_with_event,
  .formats = c(count_fraction = format_count_fraction_fixed_dp)
)

#' @describeIn count_patients_with_event Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_patients_with_event()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_patients_with_event()` to the table layout.
#'
#' @examples
#' # `count_patients_with_event()`
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_values(
#'     "STUDYID",
#'     values = "AB12345",
#'     .stats = "count",
#'     .labels = c(count = "Total AEs")
#'   ) %>%
#'   count_patients_with_event(
#'     "SUBJID",
#'     filters = c("TRTEMFL" = "Y"),
#'     .labels = c(count_fraction = "Total number of patients with at least one adverse event"),
#'     table_names = "tbl_all"
#'   ) %>%
#'   count_patients_with_event(
#'     "SUBJID",
#'     filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
#'     .labels = c(count_fraction = "Total number of patients with fatal AEs"),
#'     table_names = "tbl_fatal"
#'   ) %>%
#'   count_patients_with_event(
#'     "SUBJID",
#'     filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL", "AEREL" = "Y"),
#'     .labels = c(count_fraction = "Total number of patients with related fatal AEs"),
#'     .indent_mods = c(count_fraction = 2L),
#'     table_names = "tbl_rel_fatal"
#'   )
#'
#' build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_patients_with_event <- function(lyt,
                                      vars,
                                      filters,
                                      riskdiff = FALSE,
                                      na_str = default_na_str(),
                                      nested = TRUE,
                                      ...,
                                      table_names = vars,
                                      .stats = "count_fraction",
                                      .formats = NULL,
                                      .labels = NULL,
                                      .indent_mods = NULL) {
  checkmate::assert_flag(riskdiff)

  s_args <- list(filters = filters, ...)

  afun <- make_afun(
    a_count_patients_with_event,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  extra_args <- if (isFALSE(riskdiff)) {
    s_args
  } else {
    list(
      afun = list("s_count_patients_with_event" = afun),
      .stats = .stats,
      .indent_mods = .indent_mods,
      s_args = s_args
    )
  }

  analyze(
    lyt,
    vars,
    afun = ifelse(isFALSE(riskdiff), afun, afun_riskdiff),
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = ifelse(length(vars) > 1, "visible", "hidden"),
    table_names = table_names
  )
}

#' Sort Data by `PK PARAM` Variable
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param pk_data (`data.frame`)\cr `Pharmacokinetics` dataframe
#' @param key_var (`character`)\cr key variable used to merge pk_data and metadata created by `d_pkparam()`
#'
#' @return A PK `data.frame` sorted by a `PARAM` variable.
#'
#' @examples
#' library(dplyr)
#'
#' adpp <- tern_ex_adpp %>% mutate(PKPARAM = factor(paste0(PARAM, " (", AVALU, ")")))
#' pk_ordered_data <- h_pkparam_sort(adpp)
#'
#' @export
h_pkparam_sort <- function(pk_data, key_var = "PARAMCD") {
  assert_df_with_variables(pk_data, list(key_var = key_var))
  pk_data$PARAMCD <- pk_data[[key_var]]

  ordered_pk_data <- d_pkparam()

  # Add the numeric values from ordered_pk_data to pk_data
  joined_data <- merge(pk_data, ordered_pk_data, by = "PARAMCD", suffixes = c("", ".y"))

  joined_data <- joined_data[, -grep(".*.y$", colnames(joined_data))]

  joined_data$TLG_ORDER <- as.numeric(joined_data$TLG_ORDER)

  # Then order PARAM based on this column
  joined_data$PARAM <- factor(joined_data$PARAM,
    levels = unique(joined_data$PARAM[order(joined_data$TLG_ORDER)]),
    ordered = TRUE
  )

  joined_data$TLG_DISPLAY <- factor(joined_data$TLG_DISPLAY,
    levels = unique(joined_data$TLG_DISPLAY[order(joined_data$TLG_ORDER)]),
    ordered = TRUE
  )

  joined_data
}

#' Control Function for Subgroup Treatment Effect Pattern (STEP) Calculations
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for STEP calculations.
#'
#' @param biomarker (`numeric` or `NULL`)\cr optional provision of the numeric biomarker variable, which
#'   could be used to infer `bandwidth`, see below.
#' @param use_percentile (`flag`)\cr if `TRUE`, the running windows are created according to
#'   quantiles rather than actual values, i.e. the bandwidth refers to the percentage of data
#'   covered in each window. Suggest `TRUE` if the biomarker variable is not uniformly
#'   distributed.
#' @param bandwidth (`number` or `NULL`)\cr indicating the bandwidth of each window.
#'   Depending on the argument `use_percentile`, it can be either the length of actual-value
#'   windows on the real biomarker scale, or percentage windows.
#'   If `use_percentile = TRUE`, it should be a number between 0 and 1.
#'   If `NULL`, treat the bandwidth to be infinity, which means only one global model will be fitted.
#'   By default, `0.25` is used for percentage windows and one quarter of the range of the `biomarker`
#'   variable for actual-value windows.
#' @param degree (`count`)\cr the degree of polynomial function of the biomarker as an interaction term
#'   with the treatment arm fitted at each window. If 0 (default), then the biomarker variable
#'   is not included in the model fitted in each biomarker window.
#' @param num_points (`count`)\cr the number of points at which the hazard ratios are estimated. The
#'   smallest number is 2.
#'
#' @return A list of components with the same names as the arguments, except `biomarker` which is
#'   just used to calculate the `bandwidth` in case that actual biomarker windows are requested.
#'
#' @examples
#' # Provide biomarker values and request actual values to be used,
#' # so that bandwidth is chosen from range.
#' control_step(biomarker = 1:10, use_percentile = FALSE)
#'
#' # Use a global model with quadratic biomarker interaction term.
#' control_step(bandwidth = NULL, degree = 2)
#'
#' # Reduce number of points to be used.
#' control_step(num_points = 10)
#'
#' @export
control_step <- function(biomarker = NULL,
                         use_percentile = TRUE,
                         bandwidth,
                         degree = 0L,
                         num_points = 39L) {
  checkmate::assert_numeric(biomarker, null.ok = TRUE)
  checkmate::assert_flag(use_percentile)
  checkmate::assert_int(num_points, lower = 2)
  checkmate::assert_count(degree)

  if (missing(bandwidth)) {
    # Infer bandwidth
    bandwidth <- if (use_percentile) {
      0.25
    } else if (!is.null(biomarker)) {
      diff(range(biomarker, na.rm = TRUE)) / 4
    } else {
      NULL
    }
  } else {
    # Check bandwidth
    if (!is.null(bandwidth)) {
      if (use_percentile) {
        assert_proportion_value(bandwidth)
      } else {
        checkmate::assert_scalar(bandwidth)
        checkmate::assert_true(bandwidth > 0)
      }
    }
  }
  list(
    use_percentile = use_percentile,
    bandwidth = bandwidth,
    degree = as.integer(degree),
    num_points = as.integer(num_points)
  )
}

#' Control function for incidence rate
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for the incidence rate, used
#' internally to specify details in `s_incidence_rate()`.
#'
#' @inheritParams argument_convention
#' @param conf_type (`string`)\cr `normal` (default), `normal_log`, `exact`, or `byar`
#'   for confidence interval type.
#' @param input_time_unit (`string`)\cr `day`, `week`, `month`, or `year` (default)
#'   indicating time unit for data input.
#' @param num_pt_year (`numeric`)\cr number of patient-years to use when calculating adverse event rates.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @seealso [incidence_rate]
#'
#' @examples
#' control_incidence_rate(0.9, "exact", "month", 100)
#'
#' @export
control_incidence_rate <- function(conf_level = 0.95,
                                   conf_type = c("normal", "normal_log", "exact", "byar"),
                                   input_time_unit = c("year", "day", "week", "month"),
                                   num_pt_year = 100) {
  conf_type <- match.arg(conf_type)
  input_time_unit <- match.arg(input_time_unit)
  checkmate::assert_number(num_pt_year)
  assert_proportion_value(conf_level)

  list(
    conf_level = conf_level,
    conf_type = conf_type,
    input_time_unit = input_time_unit,
    num_pt_year = num_pt_year
  )
}

#' Generate PK reference dataset
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @return `data.frame` of PK parameters
#'
#' @examples
#' pk_reference_dataset <- d_pkparam()
#'
#' @export
d_pkparam <- function() {
  pk_dataset <- as.data.frame(matrix(
    c(
      "TMAX", "Time of CMAX", "Tmax", "Plasma/Blood/Serum", "1",
      "CMAX", "Max Conc", "Cmax", "Plasma/Blood/Serum", "2",
      "CMAXD", "Max Conc Norm by Dose", "Cmax/D", "Plasma/Blood/Serum", "3",
      "AUCIFO", "AUC Infinity Obs", "AUCinf obs", "Plasma/Blood/Serum", "4",
      "AUCIFP", "AUC Infinity Pred", "AUCinf pred", "Plasma/Blood/Serum", "5",
      "AUCIFOD", "AUC Infinity Obs Norm by Dose", "AUCinf/D obs", "Plasma/Blood/Serum", "6",
      "AUCIFD", "AUC Infinity Pred Norm by Dose", "AUCinf/D pred", "Plasma/Blood/Serum", "7",
      "AUCPEO", "AUC %Extrapolation Obs", "AUCinf extrap obs", "Plasma/Blood/Serum", "8",
      "AUCPEP", "AUC %Extrapolation Pred", "AUCinf extrap pred", "Plasma/Blood/Serum", "9",
      "AUCINT", "AUC from T1 to T2", "AUCupper-lower ", "Plasma/Blood/Serum", "10",
      "AUCTAU", "AUC Over Dosing Interval", "AUCtau", "Plasma/Blood/Serum", "11",
      "AUCLST", "AUC to Last Nonzero Conc", "AUClast", "Plasma/Blood/Serum", "12",
      "AUCALL", "AUC All", "AUCall", "Plasma/Blood/Serum", "13",
      "AUMCIFO", "AUMC Infinity Obs", "AUMCinf obs", "Plasma/Blood/Serum", "14",
      "AUMCIFP", "AUMC Infinity Pred", "AUMCinf pred", "Plasma/Blood/Serum", "15",
      "AUMCPEO", "AUMC % Extrapolation Obs", "AUMC extrap obs", "Plasma/Blood/Serum", "16",
      "AUMCPEP", "AUMC % Extrapolation Pred", "AUMC extrap pred", "Plasma/Blood/Serum", "17",
      "AUMCTAU", "AUMC Over Dosing Interval", "AUMCtau", "Plasma/Blood/Serum", "18",
      "AUMCLST", "AUMC to Last Nonzero Conc", "AUMClast", "Plasma/Blood/Serum", "19",
      "AURCIFO", "AURC Infinity Obs", "AURCinf obs", "Plasma/Blood/Serum", "20",
      "AURCIFP", "AURC Infinity Pred", "AURCinf pred", "Plasma/Blood/Serum", "21",
      "AURCPEO", "AURC % Extrapolation Obs", "AURC extrap obs", "Plasma/Blood/Serum", "22",
      "AURCPEP", "AURC % Extrapolation Pred", "AURC extrap pred", "Plasma/Blood/Serum", "23",
      "AURCLST", "AURC Dosing to Last Conc", "AURClast", "Plasma/Blood/Serum", "24",
      "AURCALL", "AURC All", "AURCall", "Plasma/Blood/Serum", "25",
      "TLST", "Time of Last Nonzero Conc", "Tlast", "Plasma/Blood/Serum", "26",
      "CO", "Initial Conc", "CO", "Plasma/Blood/Serum", "27",
      "C0", "Initial Conc", "C0", "Plasma/Blood/Serum", "28",
      "CAVG", "Average Conc", "Cavg", "Plasma/Blood/Serum", "29",
      "CLST", "Last Nonzero Conc", "Clast", "Plasma/Blood/Serum", "30",
      "CMIN", "Min Conc", "Cmin", "Plasma/Blood/Serum", "31",
      "LAMZHL", "Half-Life Lambda z", "t1/2", "Plasma/Blood/Serum", "32",
      "CLFO", "Total CL Obs by F", "CL/F obs", "Plasma/Blood/Serum", "33",
      "CLFP", "Total CL Pred by F", "CL/F pred", "Plasma/Blood/Serum", "34",
      "CLO", "Total CL Obs", "CL obs", "Plasma/Blood/Serum", "35",
      "CLP", "Total CL Pred", "CL pred", "Plasma/Blood/Serum", "36",
      "CLSS", "Total CL Steady State Pred", "CLss", "Plasma/Blood/Serum", "37",
      "CLSSF", "Total CL Steady State Pred by F", "CLss/F", "Plasma/Blood/Serum", "38",
      "VZFO", "Vz Obs by F", "Vz/F obs", "Plasma/Blood/Serum", "39",
      "VZFP", "Vz Pred by F", "Vz/F pred", "Plasma/Blood/Serum", "40",
      "VZO", "Vz Obs", "Vz obs", "Plasma/Blood/Serum", "41",
      "VZP", "Vz Pred", "Vz pred", "Plasma/Blood/Serum", "42",
      "VSSO", "Vol Dist Steady State Obs", "Vss obs", "Plasma/Blood/Serum", "43",
      "VSSP", "Vol Dist Steady State Pred", "Vss pred", "Plasma/Blood/Serum", "44",
      "LAMZ", "Lambda z", "Lambda z", "Plasma/Blood/Serum", "45",
      "LAMZLL", "Lambda z Lower Limit", "Lambda z lower", "Plasma/Blood/Serum", "46",
      "LAMZUL", "Lambda z Upper Limit", "Lambda z upper", "Plasma/Blood/Serum", "47",
      "LAMZNPT", "Number of Points for Lambda z", "No points Lambda z", "Plasma/Blood/Serum", "48",
      "MRTIFO", "MRT Infinity Obs", "MRTinf obs", "Plasma/Blood/Serum", "49",
      "MRTIFP", "MRT Infinity Pred", "MRTinf pred", "Plasma/Blood/Serum", "50",
      "MRTLST", "MRT to Last Nonzero Conc", "MRTlast", "Plasma/Blood/Serum", "51",
      "R2", "R Squared", "Rsq", "Plasma/Blood/Serum", "52",
      "R2ADJ", "R Squared Adjusted", "Rsq adjusted", "Plasma/Blood/Serum", "53",
      "TLAG", "Time Until First Nonzero Conc", "TIag", "Plasma/Blood/Serum", "54",
      "TMIN", "Time of CMIN Observation", "Tmin", "Plasma/Blood/Serum", "55",
      "ACCI", "Accumulation Index", "Accumulation Index", "Plasma/Blood/Serum/Urine", "56",
      "FLUCP", "Fluctuation%", "Fluctuation", "Plasma/Blood/Serum", "57",
      "CORRXY", "Correlation Between TimeX and Log ConcY", "Corr xy", "Plasma/Blood/Serum", "58",
      "RCAMINT", "Amt Rec from T1 to T2", "Ae", "Urine", "59",
      "RCPCINT", "Pct Rec from T1 to T2", "Fe", "Urine", "60",
      "VOLPK", "Sum of Urine Vol", "Urine volume", "Urine", "61",
      "RENALCL", "Renal CL", "CLR", "Plasma/Blood/Serum/Urine", "62",
      "ERTMAX", "Time of Max Excretion Rate", "Tmax Rate", "Urine", "63",
      "RMAX", "Time of Maximum Response", "Rmax", "Matrix of PD", "64",
      "RMIN", "Time of Minimum Response", "Rmin", "Matrix of PD", "65",
      "ERMAX", "Max Excretion Rate", "Max excretion rate", "Urine", "66",
      "MIDPTLST", "Midpoint of Collection Interval", "Midpoint last", "Urine", "67",
      "ERLST", "Last Meas Excretion Rate", "Rate last", "Urine", "68",
      "TON", "Time to Onset", "Tonset", "Matrix of PD", "69",
      "TOFF", "Time to Offset", "Toffset", "Matrix of PD", "70",
      "TBBLP", "Time Below Baseline %", "Time %Below Baseline", "Matrix of PD", "71",
      "TBTP", "Time Below Threshold %", "Time %Below Threshold", "Matrix of PD", "72",
      "TABL", "Time Above Baseline", "Time Above Baseline", "Matrix of PD", "73",
      "TAT", "Time Above Threshold", "Time Above Threshold", "Matrix of PD", "74",
      "TBT", "Time Below Threshold", "Time Below Threshold", "Matrix of PD", "75",
      "TBLT", "Time Between Baseline and Threshold", "Time Between Baseline Threshold", "Matrix of PD", "76",
      "BLRSP", "Baseline Response", "Baseline", "Matrix of PD", "77",
      "TSHDRSP", "Response Threshold", "Threshold", "Matrix of PD", "78",
      "AUCABL", "AUC Above Baseline", "AUC above baseline", "Matrix of PD", "79",
      "AUCAT", "AUC Above Threshold", "AUC above threshold", "Matrix of PD", "80",
      "AUCBBL", "AUC Below Baseline", "AUC below baseline", "Matrix of PD", "81",
      "AUCBT", "AUC Below Threshold", "AUC below threshold", "Matrix of PD", "82",
      "AUCBLDIF", "Diff AUC Above Base and AUC Below Base", "AUC diff baseline", "Matrix of PD", "83",
      "AUCTDIF", "Diff AUC Above Thr and AUC Below Thr", "AUCnet threshold", "Matrix of PD", "84",
      "TDIFF", "Diff Time to Offset and Time to Onset", "Diff toffset-tonset", "Matrix of PD", "85",
      "AUCPBEO", "AUC %Back Extrapolation Obs", "AUC%Back extrap obs", "Plasma/Blood/Serum", "86",
      "AUCPBEP", "AUC %Back Extrapolation Pred", "AUC%Back extrap pred", "Plasma/Blood/Serum", "87",
      "TSLP1L", "Lower Time Limit Slope 1st", "Slope1 lower", "Matrix of PD", "88",
      "TSLP1U", "Upper Time Limit Slope 1st Segment", "Slope1 upper", "Matrix of PD", "89",
      "TSLP2L", "Lower Time Limit Slope 2nd Segment", "Slope2 lower", "Matrix of PD", "90",
      "TSLP2U", "Upper Time Limit Slope 2nd Segment", "Slope2 upper", "Matrix of PD", "91",
      "SLP1", "Slope, 1st Segment", "Slope1", "Matrix of PD", "92",
      "SLP2", "Slope, 2nd Segment", "Slope2", "Matrix of PD", "93",
      "SLP1PT", "Number of Points for Slope 1st Segment", "No points slope1", "Matrix of PD", "94",
      "SLP2PT", "Number of Points for Slope 2nd Segment", "No points slope2", "Matrix of PD", "95",
      "R2ADJS1", "R-Squared Adjusted Slope, 1st Segment", "Rsq adjusted slope1", "Matrix of PD", "96",
      "R2ADJS2", "R-Squared Adjusted Slope, 2nd Segment", "Rsq adjusted slope2", "Matrix of PD", "97",
      "R2SLP1", "R Squared, Slope, 1st Segment", "Rsq slope1", "Matrix of PD", "98",
      "R2SLP2", "R Squared, Slope, 2nd Segment", "Rsq slope2", "Matrix of PD", "99",
      "CORRXYS1", "Corr Btw TimeX and Log ConcY, Slope 1st", "Corr xy slope1", "Plasma/Blood/Serum", "100",
      "CORRXYS2", "Corr Btw TimeX and Log ConcY, Slope 1st Slope 2nd", "Corr xy slope2", "Plasma/Blood/Serum", "101",
      "AILAMZ", "Accumulation Index using Lambda z", "AILAMZ", "Plasma/Blood/Serum", "102",
      "ARAUC", "Accumulation Ratio AUCTAU", "ARAUC", "Plasma/Blood/Serum", "103",
      "ARAUCD", "Accum Ratio AUCTAU norm by dose", "ARAUCD", "Plasma/Blood/Serum", "104",
      "ARAUCIFO", "Accum Ratio AUC Infinity Obs", "ARAUCIFO", "Plasma/Blood/Serum", "105",
      "ARAUCIFP", "Accum Ratio AUC Infinity Pred", "ARAUCIFP", "Plasma/Blood/Serum", "106",
      "ARAUCIND", "Accum Ratio AUC T1 to T2 norm by dose", "ARAUCIND_T1_T2_UNIT", "Plasma/Blood/Serum", "107",
      "ARAUCINT", "Accumulation Ratio AUC from T1 to T2", "ARAUCINT_T1_T2_UNIT", "Plasma/Blood/Serum", "108",
      "ARAUCIOD", "Accum Ratio AUCIFO Norm by Dose", "ARAUCIOD", "Plasma/Blood/Serum", "109",
      "ARAUCIPD", "Accum Ratio AUCIFP Norm by Dose", "ARAUCIPD", "Plasma/Blood/Serum", "110",
      "ARAUCLST", "Accum Ratio AUC to Last Nonzero Conc", "ARAUCLST", "Plasma/Blood/Serum", "111",
      "ARCMAX", "Accumulation Ratio Cmax", "ARCMAX", "Plasma/Blood/Serum", "112",
      "ARCMAXD", "Accum Ratio Cmax norm by dose", "ARCMAXD", "Plasma/Blood/Serum", "113",
      "ARCMIN", "Accumulation Ratio Cmin", "ARCMIN", "Plasma/Blood/Serum", "114",
      "ARCMIND", "Accum Ratio Cmin norm by dose", "ARCMIND", "Plasma/Blood/Serum", "115",
      "ARCTROUD", "Accum Ratio Ctrough norm by dose", "ARCTROUD", "Plasma/Blood/Serum", "116",
      "ARCTROUG", "Accumulation Ratio Ctrough", "ARCTROUG", "Plasma/Blood/Serum", "117",
      "AUCALLB", "AUC All Norm by BMI", "AUCall_B", "Plasma/Blood/Serum", "118",
      "AUCALLD", "AUC All Norm by Dose", "AUCall_D", "Plasma/Blood/Serum", "119",
      "AUCALLS", "AUC All Norm by SA", "AUCall_S", "Plasma/Blood/Serum", "120",
      "AUCALLW", "AUC All Norm by WT", "AUCall_W", "Plasma/Blood/Serum", "121",
      "AUCIFOB", "AUC Infinity Obs Norm by BMI", "AUCINF_obs_B", "Plasma/Blood/Serum", "122",
      "AUCIFOLN", "AUC Infinity Obs LN Transformed", "AUCIFOLN", "Plasma/Blood/Serum", "123",
      "AUCIFOS", "AUC Infinity Obs Norm by SA", "AUCINF_obs_S", "Plasma/Blood/Serum", "124",
      "AUCIFOUB", "AUC Infinity Obs, Unbound Drug", "AUCIFOUB", "Plasma/Blood/Serum", "125",
      "AUCIFOW", "AUC Infinity Obs Norm by WT", "AUCINF_obs_W", "Plasma/Blood/Serum", "126",
      "AUCIFPB", "AUC Infinity Pred Norm by BMI", "AUCINF_pred_B", "Plasma/Blood/Serum", "127",
      "AUCIFPD", "AUC Infinity Pred Norm by Dose", "AUCINF_pred_D", "Plasma/Blood/Serum", "128",
      "AUCIFPS", "AUC Infinity Pred Norm by SA", "AUCINF_pred_S", "Plasma/Blood/Serum", "129",
      "AUCIFPUB", "AUC Infinity Pred, Unbound Drug", "AUCIFPUB", "Plasma/Blood/Serum", "130",
      "AUCIFPW", "AUC Infinity Pred Norm by WT", "AUCINF_pred_W", "Plasma/Blood/Serum", "131",
      "AUCINTB", "AUC from T1 to T2 Norm by BMI", "AUC_B_T1_T2_UNIT", "Plasma/Blood/Serum", "132",
      "AUCINTD", "AUC from T1 to T2 Norm by Dose", "AUC_D_T1_T2_UNIT", "Plasma/Blood/Serum", "133",
      "AUCINTS", "AUC from T1 to T2 Norm by SA", "AUC_S_T1_T2_UNIT", "Plasma/Blood/Serum", "134",
      "AUCINTW", "AUC from T1 to T2 Norm by WT", "AUC_W_T1_T2_UNIT", "Plasma/Blood/Serum", "135",
      "AUCLSTB", "AUC to Last Nonzero Conc Norm by BMI", "AUClast_B", "Plasma/Blood/Serum", "136",
      "AUCLSTD", "AUC to Last Nonzero Conc Norm by Dose", "AUClast_D", "Plasma/Blood/Serum", "137",
      "AUCLSTLN", "AUC to Last Nonzero Conc LN Transformed", "AUCLSTLN", "Plasma/Blood/Serum", "138",
      "AUCLSTS", "AUC to Last Nonzero Conc Norm by SA", "AUClast_S", "Plasma/Blood/Serum", "139",
      "AUCLSTUB", "AUC to Last Nonzero Conc, Unbound Drug", "AUCLSTUB", "Plasma/Blood/Serum", "140",
      "AUCLSTW", "AUC to Last Nonzero Conc Norm by WT", "AUClast_W", "Plasma/Blood/Serum", "141",
      "AUCTAUB", "AUC Over Dosing Interval Norm by BMI", "AUC_TAU_B", "Plasma/Blood/Serum", "142",
      "AUCTAUD", "AUC Over Dosing Interval Norm by Dose", "AUC_TAU_D", "Plasma/Blood/Serum", "143",
      "AUCTAUS", "AUC Over Dosing Interval Norm by SA", "AUC_TAU_S", "Plasma/Blood/Serum", "144",
      "AUCTAUW", "AUC Over Dosing Interval Norm by WT", "AUC_TAU_W", "Plasma/Blood/Serum", "145",
      "AUMCIFOB", "AUMC Infinity Obs Norm by BMI", "AUMCINF_obs_B", "Plasma/Blood/Serum", "146",
      "AUMCIFOD", "AUMC Infinity Obs Norm by Dose", "AUMCINF_obs_D", "Plasma/Blood/Serum", "147",
      "AUMCIFOS", "AUMC Infinity Obs Norm by SA", "AUMCINF_obs_S", "Plasma/Blood/Serum", "148",
      "AUMCIFOW", "AUMC Infinity Obs Norm by WT", "AUMCINF_obs_W", "Plasma/Blood/Serum", "149",
      "AUMCIFPB", "AUMC Infinity Pred Norm by BMI", "AUMCINF_pred_B", "Plasma/Blood/Serum", "150",
      "AUMCIFPD", "AUMC Infinity Pred Norm by Dose", "AUMCINF_pred_D", "Plasma/Blood/Serum", "151",
      "AUMCIFPS", "AUMC Infinity Pred Norm by SA", "AUMCINF_pred_S", "Plasma/Blood/Serum", "152",
      "AUMCIFPW", "AUMC Infinity Pred Norm by WT", "AUMCINF_pred_W", "Plasma/Blood/Serum", "153",
      "AUMCLSTB", "AUMC to Last Nonzero Conc Norm by BMI", "AUMClast_B", "Plasma/Blood/Serum", "154",
      "AUMCLSTD", "AUMC to Last Nonzero Conc Norm by Dose", "AUMClast_D", "Plasma/Blood/Serum", "155",
      "AUMCLSTS", "AUMC to Last Nonzero Conc Norm by SA", "AUMClast_S", "Plasma/Blood/Serum", "156",
      "AUMCLSTW", "AUMC to Last Nonzero Conc Norm by WT", "AUMClast_W", "Plasma/Blood/Serum", "157",
      "AUMCTAUB", "AUMC Over Dosing Interval Norm by BMI", "AUMCTAUB", "Plasma/Blood/Serum", "158",
      "AUMCTAUD", "AUMC Over Dosing Interval Norm by Dose", "AUMCTAUD", "Plasma/Blood/Serum", "159",
      "AUMCTAUS", "AUMC Over Dosing Interval Norm by SA", "AUMCTAUS", "Plasma/Blood/Serum", "160",
      "AUMCTAUW", "AUMC Over Dosing Interval Norm by WT", "AUMCTAUW", "Plasma/Blood/Serum", "161",
      "AURCALLB", "AURC All Norm by BMI", "AURCALLB", "Plasma/Blood/Serum", "162",
      "AURCALLD", "AURC All Norm by Dose", "AURCALLD", "Plasma/Blood/Serum", "163",
      "AURCALLS", "AURC All Norm by SA", "AURCALLS", "Plasma/Blood/Serum", "164",
      "AURCALLW", "AURC All Norm by WT", "AURCALLW", "Plasma/Blood/Serum", "165",
      "AURCIFOB", "AURC Infinity Obs Norm by BMI", "AURCIFOB", "Plasma/Blood/Serum", "166",
      "AURCIFOD", "AURC Infinity Obs Norm by Dose", "AURCIFOD", "Plasma/Blood/Serum", "167",
      "AURCIFOS", "AURC Infinity Obs Norm by SA", "AURCIFOS", "Plasma/Blood/Serum", "168",
      "AURCIFOW", "AURC Infinity Obs Norm by WT", "AURCIFOW", "Plasma/Blood/Serum", "169",
      "AURCIFPB", "AURC Infinity Pred Norm by BMI", "AURCIFPB", "Plasma/Blood/Serum", "170",
      "AURCIFPD", "AURC Infinity Pred Norm by Dose", "AURCIFPD", "Plasma/Blood/Serum", "171",
      "AURCIFPS", "AURC Infinity Pred Norm by SA", "AURCIFPS", "Plasma/Blood/Serum", "172",
      "AURCIFPW", "AURC Infinity Pred Norm by WT", "AURCIFPW", "Plasma/Blood/Serum", "173",
      "AURCINT", "AURC from T1 to T2", "AURCINT_T1_T2_UNIT", "Plasma/Blood/Serum", "174",
      "AURCINTB", "AURC from T1 to T2 Norm by BMI", "AURCINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "175",
      "AURCINTD", "AURC from T1 to T2 Norm by Dose", "AURCINTD_T1_T2_UNIT", "Plasma/Blood/Serum", "176",
      "AURCINTS", "AURC from T1 to T2 Norm by SA", "AURCINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "177",
      "AURCINTW", "AURC from T1 to T2 Norm by WT", "AURCINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "178",
      "AURCLSTB", "AURC to Last Nonzero Rate Norm by BMI", "AURCLSTB", "Plasma/Blood/Serum", "179",
      "AURCLSTD", "AURC to Last Nonzero Rate Norm by Dose", "AURCLSTD", "Plasma/Blood/Serum", "180",
      "AURCLSTS", "AURC to Last Nonzero Rate Norm by SA", "AURCLSTS", "Plasma/Blood/Serum", "181",
      "AURCLSTW", "AURC to Last Nonzero Rate Norm by WT", "AURCLSTW", "Plasma/Blood/Serum", "182",
      "C0B", "Initial Conc Norm by BMI", "C0B", "Plasma/Blood/Serum", "183",
      "C0D", "Initial Conc Norm by Dose", "C0D", "Plasma/Blood/Serum", "184",
      "C0S", "Initial Conc Norm by SA", "C0S", "Plasma/Blood/Serum", "185",
      "C0W", "Initial Conc Norm by WT", "C0W", "Plasma/Blood/Serum", "186",
      "CAVGB", "Average Conc Norm by BMI", "CAVGB", "Plasma/Blood/Serum", "187",
      "CAVGD", "Average Conc Norm by Dose", "CAVGD", "Plasma/Blood/Serum", "188",
      "CAVGINT", "Average Conc from T1 to T2", "CAVGINT_T1_T2_UNIT", "Plasma/Blood/Serum", "189",
      "CAVGINTB", "Average Conc from T1 to T2 Norm by BMI", "CAVGINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "190",
      "CAVGINTD", "Average Conc from T1 to T2 Norm by Dose", "CAVGINTD_T1_T2_UNIT", "Plasma/Blood/Serum", "191",
      "CAVGINTS", "Average Conc from T1 to T2 Norm by SA", "CAVGINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "192",
      "CAVGINTW", "Average Conc from T1 to T2 Norm by WT", "CAVGINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "193",
      "CAVGS", "Average Conc Norm by SA", "CAVGS", "Plasma/Blood/Serum", "194",
      "CAVGW", "Average Conc Norm by WT", "CAVGW", "Plasma/Blood/Serum", "195",
      "CHTMAX", "Concentration at Half Tmax", "CHTMAX", "Plasma/Blood/Serum", "196",
      "CLFOB", "Total CL Obs by F Norm by BMI", "CLFOB", "Plasma/Blood/Serum", "197",
      "CLFOD", "Total CL Obs by F Norm by Dose", "CLFOD", "Plasma/Blood/Serum", "198",
      "CLFOS", "Total CL Obs by F Norm by SA", "CLFOS", "Plasma/Blood/Serum", "199",
      "CLFOW", "Total CL Obs by F Norm by WT", "CLFOW", "Plasma/Blood/Serum", "200",
      "CLFPB", "Total CL Pred by F Norm by BMI", "CLFPB", "Plasma/Blood/Serum", "201",
      "CLFPD", "Total CL Pred by F Norm by Dose", "CLFPD", "Plasma/Blood/Serum", "202",
      "CLFPS", "Total CL Pred by F Norm by SA", "CLFPS", "Plasma/Blood/Serum", "203",
      "CLFPW", "Total CL Pred by F Norm by WT", "CLFPW", "Plasma/Blood/Serum", "204",
      "CLFTAU", "Total CL by F for Dose Int", "CLFTAU", "Plasma/Blood/Serum", "205",
      "CLFTAUB", "Total CL by F for Dose Int Norm by BMI", "CLFTAUB", "Plasma/Blood/Serum", "206",
      "CLFTAUD", "Total CL by F for Dose Int Norm by Dose", "CLFTAUD", "Plasma/Blood/Serum", "207",
      "CLFTAUS", "Total CL by F for Dose Int Norm by SA", "CLFTAUS", "Plasma/Blood/Serum", "208",
      "CLFTAUW", "Total CL by F for Dose Int Norm by WT", "CLFTAUW", "Plasma/Blood/Serum", "209",
      "CLFUB", "Apparent CL for Unbound Drug", "CLFUB", "Plasma/Blood/Serum", "210",
      "CLOB", "Total CL Obs Norm by BMI", "CLOB", "Plasma/Blood/Serum", "211",
      "CLOD", "Total CL Obs Norm by Dose", "CLOD", "Plasma/Blood/Serum", "212",
      "CLOS", "Total CL Obs Norm by SA", "CLOS", "Plasma/Blood/Serum", "213",
      "CLOUB", "Total CL Obs for Unbound Drug", "CLOUB", "Plasma/Blood/Serum", "214",
      "CLOW", "Total CL Obs Norm by WT", "CLOW", "Plasma/Blood/Serum", "215",
      "CLPB", "Total CL Pred Norm by BMI", "CLPB", "Plasma/Blood/Serum", "216",
      "CLPD", "Total CL Pred Norm by Dose", "CLPD", "Plasma/Blood/Serum", "217",
      "CLPS", "Total CL Pred Norm by SA", "CLPS", "Plasma/Blood/Serum", "218",
      "CLPUB", "Total CL Pred for Unbound Drug", "CLPUB", "Plasma/Blood/Serum", "219",
      "CLPW", "Total CL Pred Norm by WT", "CLPW", "Plasma/Blood/Serum", "220",
      "CLRPCLEV", "Renal CL as Pct CL EV", "CLRPCLEV", "Urine", "221",
      "CLRPCLIV", "Renal CL as Pct CL IV", "CLRPCLIV", "Urine", "222",
      "CLSTB", "Last Nonzero Conc Norm by BMI", "CLSTB", "Plasma/Blood/Serum", "223",
      "CLSTD", "Last Nonzero Conc Norm by Dose", "CLSTD", "Plasma/Blood/Serum", "224",
      "CLSTS", "Last Nonzero Conc Norm by SA", "CLSTS", "Plasma/Blood/Serum", "225",
      "CLSTW", "Last Nonzero Conc Norm by WT", "CLSTW", "Plasma/Blood/Serum", "226",
      "CLTAU", "Total CL for Dose Int", "CLTAU", "Plasma/Blood/Serum", "227",
      "CLTAUB", "Total CL for Dose Int Norm by BMI", "CLTAUB", "Plasma/Blood/Serum", "228",
      "CLTAUD", "Total CL for Dose Int Norm by Dose", "CLTAUD", "Plasma/Blood/Serum", "229",
      "CLTAUS", "Total CL for Dose Int Norm by SA", "CLTAUS", "Plasma/Blood/Serum", "230",
      "CLTAUW", "Total CL for Dose Int Norm by WT", "CLTAUW", "Plasma/Blood/Serum", "231",
      "CMAXB", "Max Conc Norm by BMI", "CMAX_B", "Plasma/Blood/Serum", "232",
      "CMAXLN", "Max Conc LN Transformed", "CMAXLN", "Plasma/Blood/Serum", "233",
      "CMAXS", "Max Conc Norm by SA", "CMAXS", "Plasma/Blood/Serum", "234",
      "CMAXUB", "Max Conc, Unbound Drug", "CMAXUB", "Plasma/Blood/Serum", "235",
      "CMAXW", "Max Conc Norm by WT", "CMAXW", "Plasma/Blood/Serum", "236",
      "CMINB", "Min Conc Norm by BMI", "CMINB", "Plasma/Blood/Serum", "237",
      "CMIND", "Min Conc Norm by Dose", "CMIND", "Plasma/Blood/Serum", "238",
      "CMINS", "Min Conc Norm by SA", "CMINS", "Plasma/Blood/Serum", "239",
      "CMINW", "Min Conc Norm by WT", "CMINW", "Plasma/Blood/Serum", "240",
      "CONC", "Concentration", "CONC", "Plasma/Blood/Serum", "241",
      "CONCB", "Conc by BMI", "CONCB", "Plasma/Blood/Serum", "242",
      "CONCD", "Conc by Dose", "CONCD", "Plasma/Blood/Serum", "243",
      "CONCS", "Conc by SA", "CONCS", "Plasma/Blood/Serum", "244",
      "CONCW", "Conc by WT", "CONCW", "Plasma/Blood/Serum", "245",
      "CTROUGH", "Conc Trough", "CTROUGH", "Plasma/Blood/Serum", "246",
      "CTROUGHB", "Conc Trough by BMI", "CTROUGHB", "Plasma/Blood/Serum", "247",
      "CTROUGHD", "Conc Trough by Dose", "CTROUGHD", "Plasma/Blood/Serum", "248",
      "CTROUGHS", "Conc Trough by SA", "CTROUGHS", "Plasma/Blood/Serum", "249",
      "CTROUGHW", "Conc Trough by WT", "CTROUGHW", "Plasma/Blood/Serum", "250",
      "EFFHL", "Effective Half-Life", "EFFHL", "Plasma/Blood/Serum", "251",
      "ERINT", "Excret Rate from T1 to T2", "ERINT_T1_T2_UNIT", "Plasma/Blood/Serum", "252",
      "ERINTB", "Excret Rate from T1 to T2 Norm by BMI", "ERINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "253",
      "ERINTD", "Excret Rate from T1 to T2 Norm by Dose", "ERINTD_T1_T2_UNIT", "Plasma/Blood/Serum", "254",
      "ERINTS", "Excret Rate from T1 to T2 Norm by SA", "ERINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "255",
      "ERINTW", "Excret Rate from T1 to T2 Norm by WT", "ERINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "256",
      "ERLSTB", "Last Meas Excretion Rate Norm by BMI", "ERLSTB", "Plasma/Blood/Serum", "257",
      "ERLSTD", "Last Meas Excretion Rate Norm by Dose", "ERLSTD", "Plasma/Blood/Serum", "258",
      "ERLSTS", "Last Meas Excretion Rate Norm by SA", "ERLSTS", "Plasma/Blood/Serum", "259",
      "ERLSTW", "Last Meas Excretion Rate Norm by WT", "ERLSTW", "Plasma/Blood/Serum", "260",
      "ERMAXB", "Max Excretion Rate Norm by BMI", "ERMAXB", "Plasma/Blood/Serum", "261",
      "ERMAXD", "Max Excretion Rate Norm by Dose", "ERMAXD", "Plasma/Blood/Serum", "262",
      "ERMAXS", "Max Excretion Rate Norm by SA", "ERMAXS", "Plasma/Blood/Serum", "263",
      "ERMAXW", "Max Excretion Rate Norm by WT", "ERMAXW", "Plasma/Blood/Serum", "264",
      "ERTLST", "Midpoint of Interval of Last Nonzero ER", "ERTLST", "Plasma/Blood/Serum", "265",
      "FABS", "Absolute Bioavailability", "FABS", "Plasma/Blood/Serum", "266",
      "FB", "Fraction Bound", "FB", "Plasma/Blood/Serum", "267",
      "FREL", "Relative Bioavailability", "FREL", "Plasma/Blood/Serum", "268",
      "FREXINT", "Fract Excr from T1 to T2", "FREXINT_T1_T2_UNIT", "Plasma/Blood/Serum", "269",
      "FU", "Fraction Unbound", "FU", "Plasma/Blood/Serum", "270",
      "HDCL", "Hemodialysis Clearance", "HDCL", "Plasma/Blood/Serum", "271",
      "HDER", "Hemodialysis Extraction Ratio", "HDER", "Plasma/Blood/Serum", "272",
      "HTMAX", "Half Tmax", "HTMAX", "Plasma/Blood/Serum", "273",
      "LAMZLTAU", "Lambda z Lower Limit TAU", "LAMZLTAU", "Plasma/Blood/Serum", "274",
      "LAMZNTAU", "Number of Points for Lambda z TAU", "LAMZNTAU", "Plasma/Blood/Serum", "275",
      "LAMZSPN", "Lambda z Span", "LAMZSPN", "Plasma/Blood/Serum", "276",
      "LAMZTAU", "Lambda z TAU", "LAMZTAU", "Plasma/Blood/Serum", "277",
      "LAMZUTAU", "Lambda z Upper Limit TAU", "LAMZUTAU", "Plasma/Blood/Serum", "278",
      "MAT", "Mean Absorption Time", "MAT", "Plasma/Blood/Serum", "279",
      "MRAUCIFO", "Metabolite Ratio for AUC Infinity Obs", "MRAUCIFO", "Plasma/Blood/Serum", "280",
      "MRAUCIFP", "Metabolite Ratio for AUC Infinity Pred", "MRAUCIFP", "Plasma/Blood/Serum", "281",
      "MRAUCINT", "Metabolite Ratio AUC from T1 to T2", "MRAUCINT_T1_T2_UNIT", "Plasma/Blood/Serum", "282",
      "MRAUCLST", "Metabolite Ratio AUC Last Nonzero Conc", "MRAUCLST", "Plasma/Blood/Serum", "283",
      "MRAUCTAU", "Metabolite Ratio for AUC Dosing Interval", "MRAUCTAU", "Plasma/Blood/Serum", "284",
      "MRCMAX", "Metabolite Ratio for Max Conc", "MRCMAX", "Plasma/Blood/Serum", "285",
      "MRTEVIFO", "MRT Extravasc Infinity Obs", "MRTEVIFO", "Plasma/Blood/Serum", "286",
      "MRTEVIFP", "MRT Extravasc Infinity Pred", "MRTEVIFP", "Plasma/Blood/Serum", "287",
      "MRTEVLST", "MRT Extravasc to Last Nonzero Conc", "MRTEVLST", "Plasma/Blood/Serum", "288",
      "MRTIVIFO", "MRT Intravasc Infinity Obs", "MRTIVIFO", "Plasma/Blood/Serum", "289",
      "MRTIVIFP", "MRT Intravasc Infinity Pred", "MRTIVIFP", "Plasma/Blood/Serum", "290",
      "MRTIVLST", "MRT Intravasc to Last Nonzero Conc", "MRTIVLST", "Plasma/Blood/Serum", "291",
      "NRENALCL", "Nonrenal CL", "NRENALCL", "Urine", "292",
      "NRENLCLB", "Nonrenal CL Norm by BMI", "NRENLCLB", "Urine", "293",
      "NRENLCLD", "Nonrenal CL Norm by Dose", "NRENLCLD", "Urine", "294",
      "NRENLCLS", "Nonrenal CL Norm by SA", "NRENLCLS", "Urine", "295",
      "NRENLCLW", "Nonrenal CL Norm by WT", "NRENLCLW", "Urine", "296",
      "PTROUGHR", "Peak Trough Ratio", "PTROUGHR", "Plasma/Blood/Serum", "297",
      "RAAUC", "Ratio AUC", "RAAUC", "Plasma/Blood/Serum", "298",
      "RAAUCIFO", "Ratio AUC Infinity Obs", "RAAUCIFO", "Plasma/Blood/Serum", "299",
      "RAAUCIFP", "Ratio AUC Infinity Pred", "RAAUCIFP", "Plasma/Blood/Serum", "300",
      "RACMAX", "Ratio CMAX", "RACMAX", "Plasma/Blood/Serum", "301",
      "RAMAXMIN", "Ratio of CMAX to CMIN", "RAMAXMIN", "Plasma/Blood/Serum", "302",
      "RCAMIFO", "Amt Rec Infinity Obs", "RCAMIFO", "Plasma/Blood/Serum", "303",
      "RCAMIFOB", "Amt Rec Infinity Obs Norm by BMI", "RCAMIFOB", "Plasma/Blood/Serum", "304",
      "RCAMIFOS", "Amt Rec Infinity Obs Norm by SA", "RCAMIFOS", "Plasma/Blood/Serum", "305",
      "RCAMIFOW", "Amt Rec Infinity Obs Norm by WT", "RCAMIFOW", "Plasma/Blood/Serum", "306",
      "RCAMIFP", "Amt Rec Infinity Pred", "RCAMIFP", "Plasma/Blood/Serum", "307",
      "RCAMIFPB", "Amt Rec Infinity Pred Norm by BMI", "RCAMIFPB", "Plasma/Blood/Serum", "308",
      "RCAMIFPS", "Amt Rec Infinity Pred Norm by SA", "RCAMIFPS", "Plasma/Blood/Serum", "309",
      "RCAMIFPW", "Amt Rec Infinity Pred Norm by WT", "RCAMIFPW", "Plasma/Blood/Serum", "310",
      "RCAMINTB", "Amt Rec from T1 to T2 Norm by BMI", "RCAMINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "311",
      "RCAMINTS", "Amt Rec from T1 to T2 Norm by SA", "RCAMINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "312",
      "RCAMINTW", "Amt Rec from T1 to T2 Norm by WT", "RCAMINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "313",
      "RCAMTAU", "Amt Rec Over Dosing Interval", "RCAMTAU", "Plasma/Blood/Serum", "314",
      "RCAMTAUB", "Amt Rec Over Dosing Interval Norm by BMI", "RCAMTAUB", "Plasma/Blood/Serum", "315",
      "RCAMTAUS", "Amt Rec Over Dosing Interval Norm by SA", "RCAMTAUS", "Plasma/Blood/Serum", "316",
      "RCAMTAUW", "Amt Rec Over Dosing Interval Norm by WT", "RCAMTAUW", "Plasma/Blood/Serum", "317",
      "RCPCIFO", "Pct Rec Infinity Obs", "RCPCIFO", "Plasma/Blood/Serum", "318",
      "RCPCIFOB", "Pct Rec Infinity Obs Norm by BMI", "RCPCIFOB", "Plasma/Blood/Serum", "319",
      "RCPCIFOS", "Pct Rec Infinity Obs Norm by SA", "RCPCIFOS", "Plasma/Blood/Serum", "320",
      "RCPCIFOW", "Pct Rec Infinity Obs Norm by WT", "RCPCIFOW", "Plasma/Blood/Serum", "321",
      "RCPCIFP", "Pct Rec Infinity Pred", "RCPCIFP", "Plasma/Blood/Serum", "322",
      "RCPCIFPB", "Pct Rec Infinity Pred Norm by BMI", "RCPCIFPB", "Plasma/Blood/Serum", "323",
      "RCPCIFPS", "Pct Rec Infinity Pred Norm by SA", "RCPCIFPS", "Plasma/Blood/Serum", "324",
      "RCPCIFPW", "Pct Rec Infinity Pred Norm by WT", "RCPCIFPW", "Plasma/Blood/Serum", "325",
      "RCPCINTB", "Pct Rec from T1 to T2 Norm by BMI", "RCPCINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "326",
      "RCPCINTS", "Pct Rec from T1 to T2 Norm by SA", "RCPCINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "327",
      "RCPCINTW", "Pct Rec from T1 to T2 Norm by WT", "RCPCINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "328",
      "RCPCLST", "Pct Rec to Last Nonzero Conc", "RCPCLST", "Plasma/Blood/Serum", "329",
      "RCPCTAU", "Pct Rec Over Dosing Interval", "RCPCTAU", "Plasma/Blood/Serum", "330",
      "RCPCTAUB", "Pct Rec Over Dosing Interval Norm by BMI", "RCPCTAUB", "Plasma/Blood/Serum", "331",
      "RCPCTAUS", "Pct Rec Over Dosing Interval Norm by SA", "RCPCTAUS", "Plasma/Blood/Serum", "332",
      "RCPCTAUW", "Pct Rec Over Dosing Interval Norm by WT", "RCPCTAUW", "Plasma/Blood/Serum", "333",
      "RENALCLB", "Renal CL Norm by BMI", "RENALCLB", "Urine", "334",
      "RENALCLD", "Renal CL Norm by Dose", "RENALCLD", "Urine", "335",
      "RENALCLS", "Renal CL Norm by SA", "RENALCLS", "Urine", "336",
      "RENALCLW", "Renal CL Norm by WT", "RENALCLW", "Urine", "337",
      "RENCLTAU", "Renal CL for Dose Int", "RENCLTAU", "Urine", "338",
      "RNCLINT", "Renal CL from T1 to T2", "RNCLINT_T1_T2_UNIT", "Urine", "339",
      "RNCLINTB", "Renal CL from T1 to T2 Norm by BMI", "RNCLINTB_T1_T2_UNIT", "Urine", "340",
      "RNCLINTD", "Renal CL from T1 to T2 Norm by Dose", "RNCLINTD_T1_T2_UNIT", "Urine", "341",
      "RNCLINTS", "Renal CL from T1 to T2 Norm by SA", "RNCLINTS_T1_T2_UNIT", "Urine", "342",
      "RNCLINTW", "Renal CL from T1 to T2 Norm by WT", "RNCLINTW_T1_T2_UNIT", "Urine", "343",
      "RNCLTAUB", "Renal CL for Dose Int Norm by BMI", "RNCLTAUB", "Urine", "344",
      "RNCLTAUD", "Renal CL for Dose Int Norm by Dose", "RNCLTAUD", "Urine", "345",
      "RNCLTAUS", "Renal CL for Dose Int Norm by SA", "RNCLTAUS", "Urine", "346",
      "RNCLTAUW", "Renal CL for Dose Int Norm by WT", "RNCLTAUW", "Urine", "347",
      "RNCLUB", "Renal CL for Unbound Drug", "RNCLUB", "Urine", "348",
      "SRAUC", "Stationarity Ratio AUC", "SRAUC", "Plasma/Blood/Serum", "349",
      "SWING", "Swing", "SWING", "Plasma/Blood/Serum", "350",
      "TAUHL", "Half-Life TAU", "TAUHL", "Plasma/Blood/Serum", "351",
      "TBBL", "Time Below Baseline", "Time_Below_B", "Plasma/Blood/Serum", "352",
      "TROUGHPR", "Trough Peak Ratio", "TROUGHPR", "Plasma/Blood/Serum", "353",
      "V0", "Vol Dist Initial", "V0", "Plasma/Blood/Serum", "354",
      "V0B", "Vol Dist Initial Norm by BMI", "V0B", "Plasma/Blood/Serum", "355",
      "V0D", "Vol Dist Initial Norm by Dose", "V0D", "Plasma/Blood/Serum", "356",
      "V0S", "Vol Dist Initial Norm by SA", "V0S", "Plasma/Blood/Serum", "357",
      "V0W", "Vol Dist Initial Norm by WT", "V0W", "Plasma/Blood/Serum", "358",
      "VSSOB", "Vol Dist Steady State Obs Norm by BMI", "VSSOB", "Plasma/Blood/Serum", "359",
      "VSSOBD", "Vol Dist Steady State Obs by B", "VSSOBD", "Plasma/Blood/Serum", "360",
      "VSSOD", "Vol Dist Steady State Obs Norm by Dose", "VSSOD", "Plasma/Blood/Serum", "361",
      "VSSOF", "Vol Dist Steady State Obs by F", "VSSOF", "Plasma/Blood/Serum", "362",
      "VSSOS", "Vol Dist Steady State Obs Norm by SA", "VSSOS", "Plasma/Blood/Serum", "363",
      "VSSOUB", "Vol Dist Steady State Obs by UB", "VSSOUB", "Plasma/Blood/Serum", "364",
      "VSSOW", "Vol Dist Steady State Obs Norm by WT", "VSSOW", "Plasma/Blood/Serum", "365",
      "VSSPB", "Vol Dist Steady State Pred Norm by BMI", "VSSPB", "Plasma/Blood/Serum", "366",
      "VSSPBD", "Vol Dist Steady State Pred by B", "VSSPBD", "Plasma/Blood/Serum", "367",
      "VSSPD", "Vol Dist Steady State Pred Norm by Dose", "VSSPD", "Plasma/Blood/Serum", "368",
      "VSSPF", "Vol Dist Steady State Pred by F", "VSSPF", "Plasma/Blood/Serum", "369",
      "VSSPS", "Vol Dist Steady State Pred Norm by SA", "VSSPS", "Plasma/Blood/Serum", "370",
      "VSSPUB", "Vol Dist Steady State Pred by UB", "VSSPUB", "Plasma/Blood/Serum", "371",
      "VSSPW", "Vol Dist Steady State Pred Norm by WT", "VSSPW", "Plasma/Blood/Serum", "372",
      "VZ", "Vol Z", "Vz", "Plasma/Blood/Serum", "373",
      "VZF", "Vol Z by F", "Vz_F", "Plasma/Blood/Serum", "374",
      "VZFOB", "Vz Obs by F Norm by BMI", "VZFOB", "Plasma/Blood/Serum", "375",
      "VZFOD", "Vz Obs by F Norm by Dose", "VZFOD", "Plasma/Blood/Serum", "376",
      "VZFOS", "Vz Obs by F Norm by SA", "VZFOS", "Plasma/Blood/Serum", "377",
      "VZFOUB", "Vz Obs by F for UB", "VZFOUB", "Plasma/Blood/Serum", "378",
      "VZFOW", "Vz Obs by F Norm by WT", "VZFOW", "Plasma/Blood/Serum", "379",
      "VZFPB", "Vz Pred by F Norm by BMI", "VZFPB", "Plasma/Blood/Serum", "380",
      "VZFPD", "Vz Pred by F Norm by Dose", "VZFPD", "Plasma/Blood/Serum", "381",
      "VZFPS", "Vz Pred by F Norm by SA", "VZFPS", "Plasma/Blood/Serum", "382",
      "VZFPUB", "Vz Pred by F for UB", "VZFPUB", "Plasma/Blood/Serum", "383",
      "VZFPW", "Vz Pred by F Norm by WT", "VZFPW", "Plasma/Blood/Serum", "384",
      "VZFTAU", "Vz for Dose Int by F", "VZFTAU", "Plasma/Blood/Serum", "385",
      "VZFTAUB", "Vz for Dose Int by F Norm by BMI", "VZFTAUB", "Plasma/Blood/Serum", "386",
      "VZFTAUD", "Vz for Dose Int by F Norm by Dose", "VZFTAUD", "Plasma/Blood/Serum", "387",
      "VZFTAUS", "Vz for Dose Int by F Norm by SA", "VZFTAUS", "Plasma/Blood/Serum", "388",
      "VZFTAUW", "Vz for Dose Int by F Norm by WT", "VZFTAUW", "Plasma/Blood/Serum", "389",
      "VZOB", "Vz Obs Norm by BMI", "VZOB", "Plasma/Blood/Serum", "390",
      "VZOD", "Vz Obs Norm by Dose", "VZOD", "Plasma/Blood/Serum", "391",
      "VZOS", "Vz Obs Norm by SA", "VZOS", "Plasma/Blood/Serum", "392",
      "VZOUB", "Vz Obs for UB", "VZOUB", "Plasma/Blood/Serum", "393",
      "VZOW", "Vz Obs Norm by WT", "VZOW", "Plasma/Blood/Serum", "394",
      "VZPB", "Vz Pred Norm by BMI", "VZPB", "Plasma/Blood/Serum", "395",
      "VZPD", "Vz Pred Norm by Dose", "VZPD", "Plasma/Blood/Serum", "396",
      "VZPS", "Vz Pred Norm by SA", "VZPS", "Plasma/Blood/Serum", "397",
      "VZPUB", "Vz Pred for UB", "VZPUB", "Plasma/Blood/Serum", "398"
    ),
    ncol = 5,
    byrow = TRUE
  ))
  colnames(pk_dataset) <- c("PARAMCD", "PARAM", "TLG_DISPLAY", "MATRIX", "TLG_ORDER")
  pk_dataset
}

#' Subgroup Treatment Effect Pattern (STEP) Fit for Binary (Response) Outcome
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This fits the Subgroup Treatment Effect Pattern logistic regression models for a binary
#' (response) outcome. The treatment arm variable must have exactly 2 levels,
#' where the first one is taken as reference and the estimated odds ratios are
#' for the comparison of the second level vs. the first one.
#'
#' The (conditional) logistic regression model which is fit is:
#'
#' `response ~ arm * poly(biomarker, degree) + covariates + strata(strata)`
#'
#' where `degree` is specified by `control_step()`.
#'
#' @inheritParams argument_convention
#' @param variables (named `list` of `character`)\cr list of analysis variables:
#'   needs `response`, `arm`, `biomarker`, and optional `covariates` and `strata`.
#' @param control (named `list`)\cr combined control list from [control_step()]
#'   and [control_logistic()].
#'
#' @return A matrix of class `step`. The first part of the columns describe the
#'   subgroup intervals used for the biomarker variable, including where the
#'   center of the intervals are and their bounds. The second part of the
#'   columns contain the estimates for the treatment arm comparison.
#'
#' @note For the default degree 0 the `biomarker` variable is not included in the model.
#'
#' @seealso [control_step()] and [control_logistic()] for the available
#'   customization options.
#'
#' @examples
#' # Testing dataset with just two treatment arms.
#' library(survival)
#' library(dplyr)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(
#'     PARAMCD == "BESRSPI",
#'     ARM %in% c("B: Placebo", "A: Drug X")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to have Placebo as reference arm for Odds Ratio calculations.
#'     ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
#'     RSP = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     SEX = factor(SEX)
#'   )
#'
#' variables <- list(
#'   arm = "ARM",
#'   biomarker = "BMRKR1",
#'   covariates = "AGE",
#'   response = "RSP"
#' )
#'
#' # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup.
#' # We use a large enough bandwidth to avoid too small subgroups and linear separation in those.
#' step_matrix <- fit_rsp_step(
#'   variables = variables,
#'   data = adrs_f,
#'   control = c(control_logistic(), control_step(bandwidth = 0.9))
#' )
#' dim(step_matrix)
#' head(step_matrix)
#'
#' # Specify different polynomial degree for the biomarker interaction to use more flexible local
#' # models. Or specify different logistic regression options, including confidence level.
#' step_matrix2 <- fit_rsp_step(
#'   variables = variables,
#'   data = adrs_f,
#'   control = c(control_logistic(conf_level = 0.9), control_step(bandwidth = NULL, degree = 1))
#' )
#'
#' # Use a global constant model. This is helpful as a reference for the subgroup models.
#' step_matrix3 <- fit_rsp_step(
#'   variables = variables,
#'   data = adrs_f,
#'   control = c(control_logistic(), control_step(bandwidth = NULL, num_points = 2L))
#' )
#'
#' # It is also possible to use strata, i.e. use conditional logistic regression models.
#' variables2 <- list(
#'   arm = "ARM",
#'   biomarker = "BMRKR1",
#'   covariates = "AGE",
#'   response = "RSP",
#'   strata = c("STRATA1", "STRATA2")
#' )
#'
#' step_matrix4 <- fit_rsp_step(
#'   variables = variables2,
#'   data = adrs_f,
#'   control = c(control_logistic(), control_step(bandwidth = NULL))
#' )
#'
#' @export
fit_rsp_step <- function(variables,
                         data,
                         control = c(control_step(), control_logistic())) {
  assert_df_with_variables(data, variables)
  checkmate::assert_list(control, names = "named")
  data <- data[!is.na(data[[variables$biomarker]]), ]
  window_sel <- h_step_window(x = data[[variables$biomarker]], control = control)
  interval_center <- window_sel$interval[, "Interval Center"]
  form <- h_step_rsp_formula(variables = variables, control = control)
  estimates <- if (is.null(control$bandwidth)) {
    h_step_rsp_est(
      formula = form,
      data = data,
      variables = variables,
      x = interval_center,
      control = control
    )
  } else {
    tmp <- mapply(
      FUN = h_step_rsp_est,
      x = interval_center,
      subset = as.list(as.data.frame(window_sel$sel)),
      MoreArgs = list(
        formula = form,
        data = data,
        variables = variables,
        control = control
      )
    )
    # Maybe we find a more elegant solution than this.
    rownames(tmp) <- c("n", "logor", "se", "ci_lower", "ci_upper")
    t(tmp)
  }
  result <- cbind(window_sel$interval, estimates)
  structure(
    result,
    class = c("step", "matrix"),
    variables = variables,
    control = control
  )
}

#' Control Function for Logistic Regression Model Fitting
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for logistic regression models.
#' `conf_level` refers to the confidence level used for the Odds Ratio CIs.
#'
#' @inheritParams argument_convention
#' @param response_definition (`string`)\cr the definition of what an event is in terms of `response`.
#'   This will be used when fitting the logistic regression model on the left hand side of the formula.
#'   Note that the evaluated expression should result in either a logical vector or a factor with 2
#'   levels. By default this is just `"response"` such that the original response variable is used
#'   and not modified further.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @examples
#' # Standard options.
#' control_logistic()
#'
#' # Modify confidence level.
#' control_logistic(conf_level = 0.9)
#'
#' # Use a different response definition.
#' control_logistic(response_definition = "I(response %in% c('CR', 'PR'))")
#'
#' @export
control_logistic <- function(response_definition = "response",
                             conf_level = 0.95) {
  checkmate::assert_true(grepl("response", response_definition))
  checkmate::assert_string(response_definition)
  assert_proportion_value(conf_level)
  list(
    response_definition = response_definition,
    conf_level = conf_level
  )
}

1		#' Helper Functions for Subgroup Treatment Effect Pattern (STEP) Calculations
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions that are used internally for the STEP calculations.
6		#'
7		#' @inheritParams argument_convention
8		#'
9		#' @name h_step
10		#' @include control_step.R
11		NULL
12
13		#' @describeIn h_step creates the windows for STEP, based on the control settings
14		#' provided.
15		#'
16		#' @param x (`numeric`)\cr biomarker value(s) to use (without `NA`).
17		#' @param control (named `list`)\cr output from `control_step()`.
18		#'
19		#' @return
20		#' * `h_step_window()` returns a list containing the window-selection matrix `sel`
21		#' and the interval information matrix `interval`.
22		#'
23		#' @export
24		h_step_window <- function(x,
25		control = control_step()) {
26	12x	checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
27	12x	checkmate::assert_list(control, names = "named")
28
29	12x	sel <- matrix(FALSE, length(x), control$num_points)
30	12x	out <- matrix(0, control$num_points, 3)
31	12x	colnames(out) <- paste("Interval", c("Center", "Lower", "Upper"))
32	12x	if (control$use_percentile) {
33		# Create windows according to percentile cutoffs.
34	9x	out <- cbind(out, out)
35	9x	colnames(out)[1:3] <- paste("Percentile", c("Center", "Lower", "Upper"))
36	9x	xs <- seq(0, 1, length.out = control$num_points + 2)[-1]
37	9x	for (i in seq_len(control$num_points)) {
38	185x	out[i, 2:3] <- c(
39	185x	max(xs[i] - control$bandwidth, 0),
40	185x	min(xs[i] + control$bandwidth, 1)
41		)
42	185x	out[i, 5:6] <- stats::quantile(x, out[i, 2:3])
43	185x	sel[, i] <- x >= out[i, 5] & x <= out[i, 6]
44		}
45		# Center is the middle point of the percentile window.
46	9x	out[, 1] <- xs[-control$num_points - 1]
47	9x	out[, 4] <- stats::quantile(x, out[, 1])
48		} else {
49		# Create windows according to cutoffs.
50	3x	m <- c(min(x), max(x))
51	3x	xs <- seq(m[1], m[2], length.out = control$num_points + 2)[-1]
52	3x	for (i in seq_len(control$num_points)) {
53	11x	out[i, 2:3] <- c(
54	11x	max(xs[i] - control$bandwidth, m[1]),
55	11x	min(xs[i] + control$bandwidth, m[2])
56		)
57	11x	sel[, i] <- x >= out[i, 2] & x <= out[i, 3]
58		}
59		# Center is the same as the point for predicting.
60	3x	out[, 1] <- xs[-control$num_points - 1]
61		}
62	12x	list(sel = sel, interval = out)
63		}
64
65		#' @describeIn h_step calculates the estimated treatment effect estimate
66		#' on the linear predictor scale and corresponding standard error from a STEP `model` fitted
67		#' on `data` given `variables` specification, for a single biomarker value `x`.
68		#' This works for both `coxph` and `glm` models, i.e. for calculating log hazard ratio or log odds
69		#' ratio estimates.
70		#'
71		#' @param model the regression model object.
72		#'
73		#' @return
74		#' * `h_step_trt_effect()` returns a vector with elements `est` and `se`.
75		#'
76		#' @export
77		h_step_trt_effect <- function(data,
78		model,
79		variables,
80		x) {
81	208x	checkmate::assert_multi_class(model, c("coxph", "glm"))
82	208x	checkmate::assert_number(x)
83	208x	assert_df_with_variables(data, variables)
84	208x	checkmate::assert_factor(data[[variables$arm]], n.levels = 2)
85
86	208x	newdata <- data[c(1, 1), ]
87	208x	newdata[, variables$biomarker] <- x
88	208x	newdata[, variables$arm] <- levels(data[[variables$arm]])
89	208x	model_terms <- stats::delete.response(stats::terms(model))
90	208x	model_frame <- stats::model.frame(model_terms, data = newdata, xlev = model$xlevels)
91	208x	mat <- stats::model.matrix(model_terms, data = model_frame, contrasts.arg = model$contrasts)
92	208x	coefs <- stats::coef(model)
93		# Note: It is important to use the coef subset from matrix, otherwise intercept and
94		# strata are included for coxph() models.
95	208x	mat <- mat[, names(coefs)]
96	208x	mat_diff <- diff(mat)
97	208x	est <- mat_diff %*% coefs
98	208x	var <- mat_diff %% stats::vcov(model) %% t(mat_diff)
99	208x	se <- sqrt(var)
100	208x	c(
101	208x	est = est,
102	208x	se = se
103		)
104		}
105
106		#' @describeIn h_step builds the model formula used in survival STEP calculations.
107		#'
108		#' @return
109		#' * `h_step_survival_formula()` returns a model formula.
110		#'
111		#' @export
112		h_step_survival_formula <- function(variables,
113		control = control_step()) {
114	10x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
115
116	10x	assert_list_of_variables(variables[c("arm", "biomarker", "event", "time")])
117	10x	form <- paste0("Surv(", variables$time, ", ", variables$event, ") ~ ", variables$arm)
118	10x	if (control$degree > 0) {
119	5x	form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
120		}
121	10x	if (!is.null(variables$covariates)) {
122	6x	form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
123		}
124	10x	if (!is.null(variables$strata)) {
125	2x	form <- paste0(form, " + strata(", paste0(variables$strata, collapse = ", "), ")")
126		}
127	10x	stats::as.formula(form)
128		}
129
130		#' @describeIn h_step estimates the model with `formula` built based on
131		#' `variables` in `data` for a given `subset` and `control` parameters for the
132		#' Cox regression.
133		#'
134		#' @param formula (`formula`)\cr the regression model formula.
135		#' @param subset (`logical`)\cr subset vector.
136		#'
137		#' @return
138		#' * `h_step_survival_est()` returns a matrix of number of observations `n`,
139		#' `events`, log hazard ratio estimates `loghr`, standard error `se`,
140		#' and Wald confidence interval bounds `ci_lower` and `ci_upper`. One row is
141		#' included for each biomarker value in `x`.
142		#'
143		#' @export
144		h_step_survival_est <- function(formula,
145		data,
146		variables,
147		x,
148		subset = rep(TRUE, nrow(data)),
149		control = control_coxph()) {
150	55x	checkmate::assert_formula(formula)
151	55x	assert_df_with_variables(data, variables)
152	55x	checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
153	55x	checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
154	55x	checkmate::assert_list(control, names = "named")
155
156		# Note: `subset` in `coxph` needs to be an expression referring to `data` variables.
157	55x	data$.subset <- subset
158	55x	coxph_warnings <- NULL
159	55x	tryCatch(
160	55x	withCallingHandlers(
161	55x	expr = {
162	55x	fit <- survival::coxph(
163	55x	formula = formula,
164	55x	data = data,
165	55x	subset = .subset,
166	55x	ties = control$ties
167		)
168		},
169	55x	warning = function(w) {
170	1x	coxph_warnings <<- c(coxph_warnings, w)
171	1x	invokeRestart("muffleWarning")
172		}
173		),
174	55x	finally = {
175		}
176		)
177	55x	if (!is.null(coxph_warnings)) {
178	1x	warning(paste(
179	1x	"Fit warnings occurred, please consider using a simpler model, or",
180	1x	"larger `bandwidth`, less `num_points` in `control_step()` settings"
181		))
182		}
183		# Produce a matrix with one row per `x` and columns `est` and `se`.
184	55x	estimates <- t(vapply(
185	55x	X = x,
186	55x	FUN = h_step_trt_effect,
187	55x	FUN.VALUE = c(1, 2),
188	55x	data = data,
189	55x	model = fit,
190	55x	variables = variables
191		))
192	55x	q_norm <- stats::qnorm((1 + control$conf_level) / 2)
193	55x	cbind(
194	55x	n = fit$n,
195	55x	events = fit$nevent,
196	55x	loghr = estimates[, "est"],
197	55x	se = estimates[, "se"],
198	55x	ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
199	55x	ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
200		)
201		}
202
203		#' @describeIn h_step builds the model formula used in response STEP calculations.
204		#'
205		#' @return
206		#' * `h_step_rsp_formula()` returns a model formula.
207		#'
208		#' @export
209		h_step_rsp_formula <- function(variables,
210		control = c(control_step(), control_logistic())) {
211	14x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
212	14x	assert_list_of_variables(variables[c("arm", "biomarker", "response")])
213	14x	response_definition <- sub(
214	14x	pattern = "response",
215	14x	replacement = variables$response,
216	14x	x = control$response_definition,
217	14x	fixed = TRUE
218		)
219	14x	form <- paste0(response_definition, " ~ ", variables$arm)
220	14x	if (control$degree > 0) {
221	8x	form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
222		}
223	14x	if (!is.null(variables$covariates)) {
224	8x	form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
225		}
226	14x	if (!is.null(variables$strata)) {
227	5x	strata_arg <- if (length(variables$strata) > 1) {
228	2x	paste0("I(interaction(", paste0(variables$strata, collapse = ", "), "))")
229		} else {
230	3x	variables$strata
231		}
232	5x	form <- paste0(form, "+ strata(", strata_arg, ")")
233		}
234	14x	stats::as.formula(form)
235		}
236
237		#' @describeIn h_step estimates the model with `formula` built based on
238		#' `variables` in `data` for a given `subset` and `control` parameters for the
239		#' logistic regression.
240		#'
241		#' @param formula (`formula`)\cr the regression model formula.
242		#' @param subset (`logical`)\cr subset vector.
243		#'
244		#' @return
245		#' * `h_step_rsp_est()` returns a matrix of number of observations `n`, log odds
246		#' ratio estimates `logor`, standard error `se`, and Wald confidence interval bounds
247		#' `ci_lower` and `ci_upper`. One row is included for each biomarker value in `x`.
248		#'
249		#' @export
250		h_step_rsp_est <- function(formula,
251		data,
252		variables,
253		x,
254		subset = rep(TRUE, nrow(data)),
255		control = control_logistic()) {
256	58x	checkmate::assert_formula(formula)
257	58x	assert_df_with_variables(data, variables)
258	58x	checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
259	58x	checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
260	58x	checkmate::assert_list(control, names = "named")
261		# Note: `subset` in `glm` needs to be an expression referring to `data` variables.
262	58x	data$.subset <- subset
263	58x	fit_warnings <- NULL
264	58x	tryCatch(
265	58x	withCallingHandlers(
266	58x	expr = {
267	58x	fit <- if (is.null(variables$strata)) {
268	54x	stats::glm(
269	54x	formula = formula,
270	54x	data = data,
271	54x	subset = .subset,
272	54x	family = stats::binomial("logit")
273		)
274		} else {
275		# clogit needs coxph and strata imported
276	4x	survival::clogit(
277	4x	formula = formula,
278	4x	data = data,
279	4x	subset = .subset
280		)
281		}
282		},
283	58x	warning = function(w) {
284	19x	fit_warnings <<- c(fit_warnings, w)
285	19x	invokeRestart("muffleWarning")
286		}
287		),
288	58x	finally = {
289		}
290		)
291	58x	if (!is.null(fit_warnings)) {
292	13x	warning(paste(
293	13x	"Fit warnings occurred, please consider using a simpler model, or",
294	13x	"larger `bandwidth`, less `num_points` in `control_step()` settings"
295		))
296		}
297		# Produce a matrix with one row per `x` and columns `est` and `se`.
298	58x	estimates <- t(vapply(
299	58x	X = x,
300	58x	FUN = h_step_trt_effect,
301	58x	FUN.VALUE = c(1, 2),
302	58x	data = data,
303	58x	model = fit,
304	58x	variables = variables
305		))
306	58x	q_norm <- stats::qnorm((1 + control$conf_level) / 2)
307	58x	cbind(
308	58x	n = length(fit$y),
309	58x	logor = estimates[, "est"],
310	58x	se = estimates[, "se"],
311	58x	ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
312	58x	ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
313		)
314		}

1		#' Multivariate Logistic Regression Table
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Layout-creating function which summarizes a logistic variable regression for binary outcome with
6		#' categorical/continuous covariates in model statement. For each covariate category (if categorical)
7		#' or specified values (if continuous), present degrees of freedom, regression parameter estimate and
8		#' standard error (SE) relative to reference group or category. Report odds ratios for each covariate
9		#' category or specified values and corresponding Wald confidence intervals as default but allow user
10		#' to specify other confidence levels. Report p-value for Wald chi-square test of the null hypothesis
11		#' that covariate has no effect on response in model containing all specified covariates.
12		#' Allow option to include one two-way interaction and present similar output for
13		#' each interaction degree of freedom.
14		#'
15		#' @inheritParams argument_convention
16		#' @param drop_and_remove_str (`character`)\cr string to be dropped and removed.
17		#'
18		#' @return A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
19		#' Adding this function to an `rtable` layout will add a logistic regression variable summary to the table layout.
20		#'
21		#' @note For the formula, the variable names need to be standard `data.frame` column names without
22		#' special characters.
23		#'
24		#' @examples
25		#' library(dplyr)
26		#' library(broom)
27		#'
28		#' adrs_f <- tern_ex_adrs %>%
29		#' filter(PARAMCD == "BESRSPI") %>%
30		#' filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
31		#' mutate(
32		#' Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
33		#' RACE = factor(RACE),
34		#' SEX = factor(SEX)
35		#' )
36		#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
37		#' mod1 <- fit_logistic(
38		#' data = adrs_f,
39		#' variables = list(
40		#' response = "Response",
41		#' arm = "ARMCD",
42		#' covariates = c("AGE", "RACE")
43		#' )
44		#' )
45		#' mod2 <- fit_logistic(
46		#' data = adrs_f,
47		#' variables = list(
48		#' response = "Response",
49		#' arm = "ARMCD",
50		#' covariates = c("AGE", "RACE"),
51		#' interaction = "AGE"
52		#' )
53		#' )
54		#'
55		#' df <- tidy(mod1, conf_level = 0.99)
56		#' df2 <- tidy(mod2, conf_level = 0.99)
57		#'
58		#' # flagging empty strings with "_"
59		#' df <- df_explicit_na(df, na_level = "_")
60		#' df2 <- df_explicit_na(df2, na_level = "_")
61		#'
62		#' result1 <- basic_table() %>%
63		#' summarize_logistic(
64		#' conf_level = 0.95,
65		#' drop_and_remove_str = "_"
66		#' ) %>%
67		#' build_table(df = df)
68		#' result1
69		#'
70		#' result2 <- basic_table() %>%
71		#' summarize_logistic(
72		#' conf_level = 0.95,
73		#' drop_and_remove_str = "_"
74		#' ) %>%
75		#' build_table(df = df2)
76		#' result2
77		#'
78		#' @export
79		#' @order 1
80		summarize_logistic <- function(lyt,
81		conf_level,
82		drop_and_remove_str = "",
83		.indent_mods = NULL) {
84		# checks
85	3x	checkmate::assert_string(drop_and_remove_str)
86
87	3x	sum_logistic_variable_test <- logistic_summary_by_flag("is_variable_summary")
88	3x	sum_logistic_term_estimates <- logistic_summary_by_flag("is_term_summary", .indent_mods = .indent_mods)
89	3x	sum_logistic_odds_ratios <- logistic_summary_by_flag("is_reference_summary", .indent_mods = .indent_mods)
90	3x	split_fun <- drop_and_remove_levels(drop_and_remove_str)
91
92	3x	lyt <- logistic_regression_cols(lyt, conf_level = conf_level)
93	3x	lyt <- split_rows_by(lyt, var = "variable", labels_var = "variable_label", split_fun = split_fun)
94	3x	lyt <- sum_logistic_variable_test(lyt)
95	3x	lyt <- split_rows_by(lyt, var = "term", labels_var = "term_label", split_fun = split_fun)
96	3x	lyt <- sum_logistic_term_estimates(lyt)
97	3x	lyt <- split_rows_by(lyt, var = "interaction", labels_var = "interaction_label", split_fun = split_fun)
98	3x	lyt <- split_rows_by(lyt, var = "reference", labels_var = "reference_label", split_fun = split_fun)
99	3x	lyt <- sum_logistic_odds_ratios(lyt)
100	3x	lyt
101		}
102
103		#' Fit for Logistic Regression
104		#'
105		#' @description `r lifecycle::badge("stable")`
106		#'
107		#' Fit a (conditional) logistic regression model.
108		#'
109		#' @inheritParams argument_convention
110		#' @param data (`data.frame`)\cr the data frame on which the model was fit.
111		#' @param response_definition (`string`)\cr the definition of what an event is in terms of `response`.
112		#' This will be used when fitting the (conditional) logistic regression model on the left hand
113		#' side of the formula.
114		#'
115		#' @return A fitted logistic regression model.
116		#'
117		#' @section Model Specification:
118		#'
119		#' The `variables` list needs to include the following elements:
120		#' * `arm`: Treatment arm variable name.
121		#' * `response`: The response arm variable name. Usually this is a 0/1 variable.
122		#' * `covariates`: This is either `NULL` (no covariates) or a character vector of covariate variable names.
123		#' * `interaction`: This is either `NULL` (no interaction) or a string of a single covariate variable name already
124		#' included in `covariates`. Then the interaction with the treatment arm is included in the model.
125		#'
126		#' @examples
127		#' library(dplyr)
128		#'
129		#' adrs_f <- tern_ex_adrs %>%
130		#' filter(PARAMCD == "BESRSPI") %>%
131		#' filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
132		#' mutate(
133		#' Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
134		#' RACE = factor(RACE),
135		#' SEX = factor(SEX)
136		#' )
137		#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
138		#' mod1 <- fit_logistic(
139		#' data = adrs_f,
140		#' variables = list(
141		#' response = "Response",
142		#' arm = "ARMCD",
143		#' covariates = c("AGE", "RACE")
144		#' )
145		#' )
146		#' mod2 <- fit_logistic(
147		#' data = adrs_f,
148		#' variables = list(
149		#' response = "Response",
150		#' arm = "ARMCD",
151		#' covariates = c("AGE", "RACE"),
152		#' interaction = "AGE"
153		#' )
154		#' )
155		#'
156		#' @export
157		fit_logistic <- function(data,
158		variables = list(
159		response = "Response",
160		arm = "ARMCD",
161		covariates = NULL,
162		interaction = NULL,
163		strata = NULL
164		),
165		response_definition = "response") {
166	74x	assert_df_with_variables(data, variables)
167	74x	checkmate::assert_subset(names(variables), c("response", "arm", "covariates", "interaction", "strata"))
168	74x	checkmate::assert_string(response_definition)
169	74x	checkmate::assert_true(grepl("response", response_definition))
170
171	74x	response_definition <- sub(
172	74x	pattern = "response",
173	74x	replacement = variables$response,
174	74x	x = response_definition,
175	74x	fixed = TRUE
176		)
177	74x	form <- paste0(response_definition, " ~ ", variables$arm)
178	74x	if (!is.null(variables$covariates)) {
179	28x	form <- paste0(form, " + ", paste(variables$covariates, collapse = " + "))
180		}
181	74x	if (!is.null(variables$interaction)) {
182	17x	checkmate::assert_string(variables$interaction)
183	17x	checkmate::assert_subset(variables$interaction, variables$covariates)
184	17x	form <- paste0(form, " + ", variables$arm, ":", variables$interaction)
185		}
186	74x	if (!is.null(variables$strata)) {
187	14x	strata_arg <- if (length(variables$strata) > 1) {
188	7x	paste0("I(interaction(", paste0(variables$strata, collapse = ", "), "))")
189		} else {
190	7x	variables$strata
191		}
192	14x	form <- paste0(form, "+ strata(", strata_arg, ")")
193		}
194	74x	formula <- stats::as.formula(form)
195	74x	if (is.null(variables$strata)) {
196	60x	stats::glm(
197	60x	formula = formula,
198	60x	data = data,
199	60x	family = stats::binomial("logit")
200		)
201		} else {
202	14x	clogit_with_tryCatch(
203	14x	formula = formula,
204	14x	data = data,
205	14x	x = TRUE
206		)
207		}
208		}
209
210		#' Custom Tidy Method for Binomial GLM Results
211		#'
212		#' @description `r lifecycle::badge("stable")`
213		#'
214		#' Helper method (for [broom::tidy()]) to prepare a data frame from a `glm` object
215		#' with `binomial` family.
216		#'
217		#' @inheritParams argument_convention
218		#' @param at (`NULL` or `numeric`)\cr optional values for the interaction variable. Otherwise the median is used.
219		#' @param x logistic regression model fitted by [stats::glm()] with "binomial" family.
220		#'
221		#' @return A `data.frame` containing the tidied model.
222		#'
223		#' @method tidy glm
224		#'
225		#' @seealso [h_logistic_regression] for relevant helper functions.
226		#'
227		#' @examples
228		#' library(dplyr)
229		#' library(broom)
230		#'
231		#' adrs_f <- tern_ex_adrs %>%
232		#' filter(PARAMCD == "BESRSPI") %>%
233		#' filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
234		#' mutate(
235		#' Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
236		#' RACE = factor(RACE),
237		#' SEX = factor(SEX)
238		#' )
239		#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
240		#' mod1 <- fit_logistic(
241		#' data = adrs_f,
242		#' variables = list(
243		#' response = "Response",
244		#' arm = "ARMCD",
245		#' covariates = c("AGE", "RACE")
246		#' )
247		#' )
248		#' mod2 <- fit_logistic(
249		#' data = adrs_f,
250		#' variables = list(
251		#' response = "Response",
252		#' arm = "ARMCD",
253		#' covariates = c("AGE", "RACE"),
254		#' interaction = "AGE"
255		#' )
256		#' )
257		#'
258		#' df <- tidy(mod1, conf_level = 0.99)
259		#' df2 <- tidy(mod2, conf_level = 0.99)
260		#'
261		#' @export
262		tidy.glm <- function(x, # nolint
263		conf_level = 0.95,
264		at = NULL,
265		...) {
266	5x	checkmate::assert_class(x, "glm")
267	5x	checkmate::assert_set_equal(x$family$family, "binomial")
268
269	5x	terms_name <- attr(stats::terms(x), "term.labels")
270	5x	xs_class <- attr(x$terms, "dataClasses")
271	5x	interaction <- terms_name[which(!terms_name %in% names(xs_class))]
272	5x	df <- if (length(interaction) == 0) {
273	2x	h_logistic_simple_terms(
274	2x	x = terms_name,
275	2x	fit_glm = x,
276	2x	conf_level = conf_level
277		)
278		} else {
279	3x	h_logistic_inter_terms(
280	3x	x = terms_name,
281	3x	fit_glm = x,
282	3x	conf_level = conf_level,
283	3x	at = at
284		)
285		}
286	5x	for (var in c("variable", "term", "interaction", "reference")) {
287	20x	df[[var]] <- factor(df[[var]], levels = unique(df[[var]]))
288		}
289	5x	df
290		}
291
292		#' Logistic Regression Multivariate Column Layout Function
293		#'
294		#' @description `r lifecycle::badge("stable")`
295		#'
296		#' Layout-creating function which creates a multivariate column layout summarizing logistic
297		#' regression results. This function is a wrapper for [rtables::split_cols_by_multivar()].
298		#'
299		#' @inheritParams argument_convention
300		#'
301		#' @return A layout object suitable for passing to further layouting functions. Adding this
302		#' function to an `rtable` layout will split the table into columns corresponding to
303		#' statistics `df`, `estimate`, `std_error`, `odds_ratio`, `ci`, and `pvalue`.
304		#'
305		#' @export
306		logistic_regression_cols <- function(lyt,
307		conf_level = 0.95) {
308	4x	vars <- c("df", "estimate", "std_error", "odds_ratio", "ci", "pvalue")
309	4x	var_labels <- c(
310	4x	df = "Degrees of Freedom",
311	4x	estimate = "Parameter Estimate",
312	4x	std_error = "Standard Error",
313	4x	odds_ratio = "Odds Ratio",
314	4x	ci = paste("Wald", f_conf_level(conf_level)),
315	4x	pvalue = "p-value"
316		)
317	4x	split_cols_by_multivar(
318	4x	lyt = lyt,
319	4x	vars = vars,
320	4x	varlabels = var_labels
321		)
322		}
323
324		#' Logistic Regression Summary Table Constructor Function
325		#'
326		#' @description `r lifecycle::badge("stable")`
327		#'
328		#' Constructor for content functions to be used in [`summarize_logistic()`] to summarize
329		#' logistic regression results. This function is a wrapper for [rtables::summarize_row_groups()].
330		#'
331		#' @inheritParams argument_convention
332		#' @param flag_var (`string`)\cr variable name identifying which row should be used in this
333		#' content function.
334		#'
335		#' @return A content function.
336		#'
337		#' @export
338		logistic_summary_by_flag <- function(flag_var, na_str = default_na_str(), .indent_mods = NULL) {
339	10x	checkmate::assert_string(flag_var)
340	10x	function(lyt) {
341	10x	cfun_list <- list(
342	10x	df = cfun_by_flag("df", flag_var, format = "xx.", .indent_mods = .indent_mods),
343	10x	estimate = cfun_by_flag("estimate", flag_var, format = "xx.xxx", .indent_mods = .indent_mods),
344	10x	std_error = cfun_by_flag("std_error", flag_var, format = "xx.xxx", .indent_mods = .indent_mods),
345	10x	odds_ratio = cfun_by_flag("odds_ratio", flag_var, format = ">999.99", .indent_mods = .indent_mods),
346	10x	ci = cfun_by_flag("ci", flag_var, format = format_extreme_values_ci(2L), .indent_mods = .indent_mods),
347	10x	pvalue = cfun_by_flag("pvalue", flag_var, format = "x.xxxx \| (<0.0001)", .indent_mods = .indent_mods)
348		)
349	10x	summarize_row_groups(
350	10x	lyt = lyt,
351	10x	cfun = cfun_list,
352	10x	na_str = na_str
353		)
354		}
355		}

1		#' Tabulate Survival Duration by Subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Tabulate statistics such as median survival time and hazard ratio for population subgroups.
6		#'
7		#' @inheritParams argument_convention
8		#' @inheritParams survival_coxph_pairwise
9		#' @param df (`list`)\cr of data frames containing all analysis variables. List should be
10		#' created using [extract_survival_subgroups()].
11		#' @param vars (`character`)\cr the name of statistics to be reported among:
12		#' * `n_tot_events`: Total number of events per group.
13		#' * `n_events`: Number of events per group.
14		#' * `n_tot`: Total number of observations per group.
15		#' * `n`: Number of observations per group.
16		#' * `median`: Median survival time.
17		#' * `hr`: Hazard ratio.
18		#' * `ci`: Confidence interval of hazard ratio.
19		#' * `pval`: p-value of the effect.
20		#' Note, one of the statistics `n_tot` and `n_tot_events`, as well as both `hr` and `ci`
21		#' are required.
22		#' @param time_unit (`string`)\cr label with unit of median survival time. Default `NULL` skips displaying unit.
23		#'
24		#' @details These functions create a layout starting from a data frame which contains
25		#' the required statistics. Tables typically used as part of forest plot.
26		#'
27		#' @seealso [extract_survival_subgroups()]
28		#'
29		#' @examples
30		#' library(dplyr)
31		#' library(forcats)
32		#'
33		#' adtte <- tern_ex_adtte
34		#'
35		#' # Save variable labels before data processing steps.
36		#' adtte_labels <- formatters::var_labels(adtte)
37		#'
38		#' adtte_f <- adtte %>%
39		#' filter(
40		#' PARAMCD == "OS",
41		#' ARM %in% c("B: Placebo", "A: Drug X"),
42		#' SEX %in% c("M", "F")
43		#' ) %>%
44		#' mutate(
45		#' # Reorder levels of ARM to display reference arm before treatment arm.
46		#' ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
47		#' SEX = droplevels(SEX),
48		#' AVALU = as.character(AVALU),
49		#' is_event = CNSR == 0
50		#' )
51		#' labels <- c(
52		#' "ARM" = adtte_labels[["ARM"]],
53		#' "SEX" = adtte_labels[["SEX"]],
54		#' "AVALU" = adtte_labels[["AVALU"]],
55		#' "is_event" = "Event Flag"
56		#' )
57		#' formatters::var_labels(adtte_f)[names(labels)] <- labels
58		#'
59		#' df <- extract_survival_subgroups(
60		#' variables = list(
61		#' tte = "AVAL",
62		#' is_event = "is_event",
63		#' arm = "ARM", subgroups = c("SEX", "BMRKR2")
64		#' ),
65		#' data = adtte_f
66		#' )
67		#' df
68		#'
69		#' df_grouped <- extract_survival_subgroups(
70		#' variables = list(
71		#' tte = "AVAL",
72		#' is_event = "is_event",
73		#' arm = "ARM", subgroups = c("SEX", "BMRKR2")
74		#' ),
75		#' data = adtte_f,
76		#' groups_lists = list(
77		#' BMRKR2 = list(
78		#' "low" = "LOW",
79		#' "low/medium" = c("LOW", "MEDIUM"),
80		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
81		#' )
82		#' )
83		#' )
84		#' df_grouped
85		#'
86		#' @name survival_duration_subgroups
87		#' @order 1
88		NULL
89
90		#' Prepares Survival Data for Population Subgroups in Data Frames
91		#'
92		#' @description `r lifecycle::badge("stable")`
93		#'
94		#' Prepares estimates of median survival times and treatment hazard ratios for population subgroups in
95		#' data frames. Simple wrapper for [h_survtime_subgroups_df()] and [h_coxph_subgroups_df()]. Result is a `list`
96		#' of two `data.frame`s: `survtime` and `hr`. `variables` corresponds to the names of variables found in `data`,
97		#' passed as a named `list` and requires elements `tte`, `is_event`, `arm` and optionally `subgroups` and `strata`.
98		#' `groups_lists` optionally specifies groupings for `subgroups` variables.
99		#'
100		#' @inheritParams argument_convention
101		#' @inheritParams survival_duration_subgroups
102		#' @inheritParams survival_coxph_pairwise
103		#'
104		#' @return A named `list` of two elements:
105		#' * `survtime`: A `data.frame` containing columns `arm`, `n`, `n_events`, `median`, `subgroup`, `var`,
106		#' `var_label`, and `row_type`.
107		#' * `hr`: A `data.frame` containing columns `arm`, `n_tot`, `n_tot_events`, `hr`, `lcl`, `ucl`, `conf_level`,
108		#' `pval`, `pval_label`, `subgroup`, `var`, `var_label`, and `row_type`.
109		#'
110		#' @seealso [survival_duration_subgroups]
111		#'
112		#' @export
113		extract_survival_subgroups <- function(variables,
114		data,
115		groups_lists = list(),
116		control = control_coxph(),
117		label_all = "All Patients") {
118	9x	if ("strat" %in% names(variables)) {
119	!	warning(
120	!	"Warning: the `strat` element name of the `variables` list argument to `extract_survival_subgroups() ",
121	!	"was deprecated in tern 0.9.3.\n ",
122	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
123		)
124	!	variables[["strata"]] <- variables[["strat"]]
125		}
126
127	9x	df_survtime <- h_survtime_subgroups_df(
128	9x	variables,
129	9x	data,
130	9x	groups_lists = groups_lists,
131	9x	label_all = label_all
132		)
133	9x	df_hr <- h_coxph_subgroups_df(
134	9x	variables,
135	9x	data,
136	9x	groups_lists = groups_lists,
137	9x	control = control,
138	9x	label_all = label_all
139		)
140
141	9x	list(survtime = df_survtime, hr = df_hr)
142		}
143
144		#' @describeIn survival_duration_subgroups Formatted analysis function which is used as
145		#' `afun` in `tabulate_survival_subgroups()`.
146		#'
147		#' @return
148		#' * `a_survival_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
149		#'
150		#' @keywords internal
151		a_survival_subgroups <- function(.formats = list( # nolint start
152		n = "xx",
153		n_events = "xx",
154		n_tot_events = "xx",
155		median = "xx.x",
156		n_tot = "xx",
157		hr = list(format_extreme_values(2L)),
158		ci = list(format_extreme_values_ci(2L)),
159		pval = "x.xxxx \| (<0.0001)"
160		),
161		na_str = default_na_str()) { # nolint end
162	15x	checkmate::assert_list(.formats)
163	15x	checkmate::assert_subset(
164	15x	names(.formats),
165	15x	c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval")
166		)
167
168	15x	afun_lst <- Map(
169	15x	function(stat, fmt, na_str) {
170	114x	if (stat == "ci") {
171	14x	function(df, labelstr = "", ...) {
172	29x	in_rows(
173	29x	.list = combine_vectors(df$lcl, df$ucl),
174	29x	.labels = as.character(df$subgroup),
175	29x	.formats = fmt,
176	29x	.format_na_strs = na_str
177		)
178		}
179		} else {
180	100x	function(df, labelstr = "", ...) {
181	159x	in_rows(
182	159x	.list = as.list(df[[stat]]),
183	159x	.labels = as.character(df$subgroup),
184	159x	.formats = fmt,
185	159x	.format_na_strs = na_str
186		)
187		}
188		}
189		},
190	15x	stat = names(.formats),
191	15x	fmt = .formats,
192	15x	na_str = na_str
193		)
194
195	15x	afun_lst
196		}
197
198		#' @describeIn survival_duration_subgroups Table-creating function which creates a table
199		#' summarizing survival by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
200		#' and [rtables::summarize_row_groups()].
201		#'
202		#' @return An `rtables` table summarizing survival by subgroup.
203		#'
204		#' @examples
205		#' ## Table with default columns.
206		#' basic_table() %>%
207		#' tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
208		#'
209		#' ## Table with a manually chosen set of columns: adding "pval".
210		#' basic_table() %>%
211		#' tabulate_survival_subgroups(
212		#' df = df,
213		#' vars = c("n_tot_events", "n_events", "median", "hr", "ci", "pval"),
214		#' time_unit = adtte_f$AVALU[1]
215		#' )
216		#'
217		#' @export
218		#' @order 2
219		tabulate_survival_subgroups <- function(lyt,
220		df,
221		vars = c("n_tot_events", "n_events", "median", "hr", "ci"),
222		groups_lists = list(),
223		label_all = "All Patients",
224		time_unit = NULL,
225		na_str = default_na_str()) {
226	6x	conf_level <- df$hr$conf_level[1]
227	6x	method <- df$hr$pval_label[1]
228
229	6x	extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method, label_all = label_all)
230
231	6x	afun_lst <- a_survival_subgroups(na_str = na_str)
232	6x	colvars <- d_survival_subgroups_colvars(
233	6x	vars,
234	6x	conf_level = conf_level,
235	6x	method = method,
236	6x	time_unit = time_unit
237		)
238
239	6x	colvars_survtime <- list(
240	6x	vars = colvars$vars[names(colvars$labels) %in% c("n", "n_events", "median")],
241	6x	labels = colvars$labels[names(colvars$labels) %in% c("n", "n_events", "median")]
242		)
243	6x	colvars_hr <- list(
244	6x	vars = colvars$vars[names(colvars$labels) %in% c("n_tot", "n_tot_events", "hr", "ci", "pval")],
245	6x	labels = colvars$labels[names(colvars$labels) %in% c("n_tot", "n_tot_events", "hr", "ci", "pval")]
246		)
247
248		# Columns from table_survtime are optional.
249	6x	if (length(colvars_survtime$vars) > 0) {
250	5x	lyt_survtime <- split_cols_by(lyt = lyt, var = "arm")
251	5x	lyt_survtime <- split_rows_by(
252	5x	lyt = lyt_survtime,
253	5x	var = "row_type",
254	5x	split_fun = keep_split_levels("content"),
255	5x	nested = FALSE
256		)
257	5x	lyt_survtime <- summarize_row_groups(
258	5x	lyt = lyt_survtime,
259	5x	var = "var_label",
260	5x	cfun = afun_lst[names(colvars_survtime$labels)],
261	5x	na_str = na_str,
262	5x	extra_args = extra_args
263		)
264	5x	lyt_survtime <- split_cols_by_multivar(
265	5x	lyt = lyt_survtime,
266	5x	vars = colvars_survtime$vars,
267	5x	varlabels = colvars_survtime$labels
268		)
269
270	5x	if ("analysis" %in% df$survtime$row_type) {
271	4x	lyt_survtime <- split_rows_by(
272	4x	lyt = lyt_survtime,
273	4x	var = "row_type",
274	4x	split_fun = keep_split_levels("analysis"),
275	4x	nested = FALSE,
276	4x	child_labels = "hidden"
277		)
278	4x	lyt_survtime <- split_rows_by(lyt = lyt_survtime, var = "var_label", nested = TRUE)
279	4x	lyt_survtime <- analyze_colvars(
280	4x	lyt = lyt_survtime,
281	4x	afun = afun_lst[names(colvars_survtime$labels)],
282	4x	na_str = na_str,
283	4x	inclNAs = TRUE,
284	4x	extra_args = extra_args
285		)
286		}
287
288	5x	table_survtime <- build_table(lyt_survtime, df = df$survtime)
289		} else {
290	1x	table_survtime <- NULL
291		}
292
293		# Columns "n_tot_events" or "n_tot", and "hr", "ci" in table_hr are required.
294	6x	lyt_hr <- split_cols_by(lyt = lyt, var = "arm")
295	6x	lyt_hr <- split_rows_by(
296	6x	lyt = lyt_hr,
297	6x	var = "row_type",
298	6x	split_fun = keep_split_levels("content"),
299	6x	nested = FALSE
300		)
301	6x	lyt_hr <- summarize_row_groups(
302	6x	lyt = lyt_hr,
303	6x	var = "var_label",
304	6x	cfun = afun_lst[names(colvars_hr$labels)],
305	6x	na_str = na_str,
306	6x	extra_args = extra_args
307		)
308	6x	lyt_hr <- split_cols_by_multivar(
309	6x	lyt = lyt_hr,
310	6x	vars = colvars_hr$vars,
311	6x	varlabels = colvars_hr$labels
312		) %>%
313	6x	append_topleft("Baseline Risk Factors")
314
315	6x	if ("analysis" %in% df$survtime$row_type) {
316	5x	lyt_hr <- split_rows_by(
317	5x	lyt = lyt_hr,
318	5x	var = "row_type",
319	5x	split_fun = keep_split_levels("analysis"),
320	5x	nested = FALSE,
321	5x	child_labels = "hidden"
322		)
323	5x	lyt_hr <- split_rows_by(lyt = lyt_hr, var = "var_label", nested = TRUE)
324	5x	lyt_hr <- analyze_colvars(
325	5x	lyt = lyt_hr,
326	5x	afun = afun_lst[names(colvars_hr$labels)],
327	5x	na_str = na_str,
328	5x	inclNAs = TRUE,
329	5x	extra_args = extra_args
330		)
331		}
332	6x	table_hr <- build_table(lyt_hr, df = df$hr)
333
334		# There can be one or two vars starting with "n_tot".
335	6x	n_tot_ids <- grep("^n_tot", colvars_hr$vars)
336	6x	if (is.null(table_survtime)) {
337	1x	result <- table_hr
338	1x	hr_id <- match("hr", colvars_hr$vars)
339	1x	ci_id <- match("lcl", colvars_hr$vars)
340		} else {
341		# Reorder the table.
342	5x	result <- cbind_rtables(table_hr[, n_tot_ids], table_survtime, table_hr[, -n_tot_ids])
343		# And then calculate column indices accordingly.
344	5x	hr_id <- length(n_tot_ids) + ncol(table_survtime) + match("hr", colvars_hr$vars[-n_tot_ids])
345	5x	ci_id <- length(n_tot_ids) + ncol(table_survtime) + match("lcl", colvars_hr$vars[-n_tot_ids])
346	5x	n_tot_ids <- seq_along(n_tot_ids)
347		}
348
349	6x	structure(
350	6x	result,
351	6x	forest_header = paste0(rev(levels(df$survtime$arm)), "\nBetter"),
352	6x	col_x = hr_id,
353	6x	col_ci = ci_id,
354		# Take the first one for scaling the symbol sizes in graph.
355	6x	col_symbol_size = n_tot_ids[1]
356		)
357		}
358
359		#' Labels for Column Variables in Survival Duration by Subgroup Table
360		#'
361		#' @description `r lifecycle::badge("stable")`
362		#'
363		#' Internal function to check variables included in [tabulate_survival_subgroups()] and create column labels.
364		#'
365		#' @inheritParams tabulate_survival_subgroups
366		#' @inheritParams argument_convention
367		#' @param method (`character`)\cr p-value method for testing hazard ratio = 1.
368		#'
369		#' @return A `list` of variables and their labels to tabulate.
370		#'
371		#' @note At least one of `n_tot` and `n_tot_events` must be provided in `vars`.
372		#'
373		#' @export
374		d_survival_subgroups_colvars <- function(vars,
375		conf_level,
376		method,
377		time_unit = NULL) {
378	15x	checkmate::assert_character(vars)
379	15x	checkmate::assert_string(time_unit, null.ok = TRUE)
380	15x	checkmate::assert_subset(c("hr", "ci"), vars)
381	15x	checkmate::assert_true(any(c("n_tot", "n_tot_events") %in% vars))
382	15x	checkmate::assert_subset(
383	15x	vars,
384	15x	c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval")
385		)
386
387	15x	propcase_time_label <- if (!is.null(time_unit)) {
388	14x	paste0("Median (", time_unit, ")")
389		} else {
390	1x	"Median"
391		}
392
393	15x	varlabels <- c(
394	15x	n = "n",
395	15x	n_events = "Events",
396	15x	median = propcase_time_label,
397	15x	n_tot = "Total n",
398	15x	n_tot_events = "Total Events",
399	15x	hr = "Hazard Ratio",
400	15x	ci = paste0(100 * conf_level, "% Wald CI"),
401	15x	pval = method
402		)
403
404	15x	colvars <- vars
405
406		# The `lcl` variable is just a placeholder available in the analysis data,
407		# it is not acutally used in the tabulation.
408		# Variables used in the tabulation are lcl and ucl, see `a_survival_subgroups` for details.
409	15x	colvars[colvars == "ci"] <- "lcl"
410
411	15x	list(
412	15x	vars = colvars,
413	15x	labels = varlabels[vars]
414		)
415		}

1		#' Summary numeric variables in columns
2		#'
3		#' @description `r lifecycle::badge("experimental")`
4		#'
5		#' Layout-creating function which can be used for creating column-wise summary tables.
6		#' This function sets the analysis methods as column labels and is a wrapper for
7		#' [rtables::analyze_colvars()]. It was designed principally for PK tables.
8		#'
9		#' @inheritParams argument_convention
10		#' @inheritParams rtables::analyze_colvars
11		#' @param imp_rule (`character`)\cr imputation rule setting. Defaults to `NULL` for no imputation rule. Can
12		#' also be `"1/3"` to implement 1/3 imputation rule or `"1/2"` to implement 1/2 imputation rule. In order
13		#' to use an imputation rule, the `avalcat_var` argument must be specified. See [imputation_rule()]
14		#' for more details on imputation.
15		#' @param avalcat_var (`character`)\cr if `imp_rule` is not `NULL`, name of variable that indicates whether a
16		#' row in the data corresponds to an analysis value in category `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or none of
17		#' the above (defaults to `"AVALCAT1"`). Variable must be present in the data and should match the variable
18		#' used to calculate the `n_blq` statistic (if included in `.stats`).
19		#' @param cache (`flag`)\cr whether to store computed values in a temporary caching environment. This will
20		#' speed up calculations in large tables, but should be set to `FALSE` if the same `rtable` layout is
21		#' used for multiple tables with different data. Defaults to `FALSE`.
22		#' @param row_labels (`character`)\cr as this function works in columns space, usual `.labels`
23		#' character vector applies on the column space. You can change the row labels by defining this
24		#' parameter to a named character vector with names corresponding to the split values. It defaults
25		#' to `NULL` and if it contains only one `string`, it will duplicate that as a row label.
26		#' @param do_summarize_row_groups (`flag`)\cr defaults to `FALSE` and applies the analysis to the current
27		#' label rows. This is a wrapper of [rtables::summarize_row_groups()] and it can accept `labelstr`
28		#' to define row labels. This behavior is not supported as we never need to overload row labels.
29		#' @param split_col_vars (`flag`)\cr defaults to `TRUE` and puts the analysis results onto the columns.
30		#' This option allows you to add multiple instances of this functions, also in a nested fashion,
31		#' without adding more splits. This split must happen only one time on a single layout.
32		#'
33		#' @return
34		#' A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
35		#' Adding this function to an `rtable` layout will summarize the given variables, arrange the output
36		#' in columns, and add it to the table layout.
37		#'
38		#' @note This is an experimental implementation of [rtables::summarize_row_groups()] and
39		#' [rtables::analyze_colvars()] that may be subjected to changes as `rtables` extends its
40		#' support to more complex analysis pipelines on the column space. For the same reasons,
41		#' we encourage to read the examples carefully and file issues for cases that differ from
42		#' them.
43		#'
44		#' Here `labelstr` behaves differently than usual. If it is not defined (default as `NULL`),
45		#' row labels are assigned automatically to the split values in case of `rtables::analyze_colvars`
46		#' (`do_summarize_row_groups = FALSE`, the default), and to the group label for
47		#' `do_summarize_row_groups = TRUE`.
48		#'
49		#' @seealso [analyze_vars()], [rtables::analyze_colvars()].
50		#'
51		#' @examples
52		#' library(dplyr)
53		#'
54		#' # Data preparation
55		#' adpp <- tern_ex_adpp %>% h_pkparam_sort()
56		#'
57		#' lyt <- basic_table() %>%
58		#' split_rows_by(var = "STRATA1", label_pos = "topleft") %>%
59		#' split_rows_by(
60		#' var = "SEX",
61		#' label_pos = "topleft",
62		#' child_labels = "hidden"
63		#' ) %>% # Removes duplicated labels
64		#' analyze_vars_in_cols(vars = "AGE")
65		#' result <- build_table(lyt = lyt, df = adpp)
66		#' result
67		#'
68		#' # By selecting just some statistics and ad-hoc labels
69		#' lyt <- basic_table() %>%
70		#' split_rows_by(var = "ARM", label_pos = "topleft") %>%
71		#' split_rows_by(
72		#' var = "SEX",
73		#' label_pos = "topleft",
74		#' child_labels = "hidden",
75		#' split_fun = drop_split_levels
76		#' ) %>%
77		#' analyze_vars_in_cols(
78		#' vars = "AGE",
79		#' .stats = c("n", "cv", "geom_mean"),
80		#' .labels = c(
81		#' n = "aN",
82		#' cv = "aCV",
83		#' geom_mean = "aGeomMean"
84		#' )
85		#' )
86		#' result <- build_table(lyt = lyt, df = adpp)
87		#' result
88		#'
89		#' # Changing row labels
90		#' lyt <- basic_table() %>%
91		#' analyze_vars_in_cols(
92		#' vars = "AGE",
93		#' row_labels = "some custom label"
94		#' )
95		#' result <- build_table(lyt, df = adpp)
96		#' result
97		#'
98		#' # Pharmacokinetic parameters
99		#' lyt <- basic_table() %>%
100		#' split_rows_by(
101		#' var = "TLG_DISPLAY",
102		#' split_label = "PK Parameter",
103		#' label_pos = "topleft",
104		#' child_labels = "hidden"
105		#' ) %>%
106		#' analyze_vars_in_cols(
107		#' vars = "AVAL"
108		#' )
109		#' result <- build_table(lyt, df = adpp)
110		#' result
111		#'
112		#' # Multiple calls (summarize label and analyze underneath)
113		#' lyt <- basic_table() %>%
114		#' split_rows_by(
115		#' var = "TLG_DISPLAY",
116		#' split_label = "PK Parameter",
117		#' label_pos = "topleft"
118		#' ) %>%
119		#' analyze_vars_in_cols(
120		#' vars = "AVAL",
121		#' do_summarize_row_groups = TRUE # does a summarize level
122		#' ) %>%
123		#' split_rows_by("SEX",
124		#' child_labels = "hidden",
125		#' label_pos = "topleft"
126		#' ) %>%
127		#' analyze_vars_in_cols(
128		#' vars = "AVAL",
129		#' split_col_vars = FALSE # avoids re-splitting the columns
130		#' )
131		#' result <- build_table(lyt, df = adpp)
132		#' result
133		#'
134		#' @export
135		analyze_vars_in_cols <- function(lyt,
136		vars,
137		...,
138		.stats = c(
139		"n",
140		"mean",
141		"sd",
142		"se",
143		"cv",
144		"geom_cv"
145		),
146		.labels = c(
147		n = "n",
148		mean = "Mean",
149		sd = "SD",
150		se = "SE",
151		cv = "CV (%)",
152		geom_cv = "CV % Geometric Mean"
153		),
154		row_labels = NULL,
155		do_summarize_row_groups = FALSE,
156		split_col_vars = TRUE,
157		imp_rule = NULL,
158		avalcat_var = "AVALCAT1",
159		cache = FALSE,
160		.indent_mods = NULL,
161		na_str = default_na_str(),
162		nested = TRUE,
163		.formats = NULL,
164		.aligns = NULL) {
165	10x	extra_args <- list(...)
166
167	10x	checkmate::assert_string(na_str, na.ok = TRUE, null.ok = TRUE)
168	10x	checkmate::assert_character(row_labels, null.ok = TRUE)
169	10x	checkmate::assert_int(.indent_mods, null.ok = TRUE)
170	10x	checkmate::assert_flag(nested)
171	10x	checkmate::assert_flag(split_col_vars)
172	10x	checkmate::assert_flag(do_summarize_row_groups)
173
174		# Filtering
175	10x	met_grps <- paste0("analyze_vars", c("_numeric", "_counts"))
176	10x	.stats <- get_stats(met_grps, stats_in = .stats)
177	10x	formats_v <- get_formats_from_stats(stats = .stats, formats_in = .formats)
178	10x	labels_v <- get_labels_from_stats(stats = .stats, labels_in = .labels)
179	!	if ("control" %in% names(extra_args)) labels_v <- labels_v %>% labels_use_control(extra_args[["control"]], .labels)
180
181		# Check for vars in the case that one or more are used
182	10x	if (length(vars) == 1) {
183	7x	vars <- rep(vars, length(.stats))
184	3x	} else if (length(vars) != length(.stats)) {
185	1x	stop(
186	1x	"Analyzed variables (vars) does not have the same ",
187	1x	"number of elements of specified statistics (.stats)."
188		)
189		}
190
191	9x	if (split_col_vars) {
192		# Checking there is not a previous identical column split
193	8x	clyt <- tail(clayout(lyt), 1)[[1]]
194
195	8x	dummy_lyt <- split_cols_by_multivar(
196	8x	lyt = basic_table(),
197	8x	vars = vars,
198	8x	varlabels = labels_v
199		)
200
201	8x	if (any(sapply(clyt, identical, y = get_last_col_split(dummy_lyt)))) {
202	!	stop(
203	!	"Column split called again with the same values. ",
204	!	"This can create many unwanted columns. Please consider adding ",
205	!	"split_col_vars = FALSE to the last call of ",
206	!	deparse(sys.calls()[[sys.nframe() - 1]]), "."
207		)
208		}
209
210		# Main col split
211	8x	lyt <- split_cols_by_multivar(
212	8x	lyt = lyt,
213	8x	vars = vars,
214	8x	varlabels = labels_v
215		)
216		}
217
218	9x	env <- new.env() # create caching environment
219
220	9x	if (do_summarize_row_groups) {
221	2x	if (length(unique(vars)) > 1) {
222	!	stop("When using do_summarize_row_groups only one label level var should be inserted.")
223		}
224
225		# Function list for do_summarize_row_groups. Slightly different handling of labels
226	2x	cfun_list <- Map(
227	2x	function(stat, use_cache, cache_env) {
228	12x	function(u, .spl_context, labelstr, .df_row, ...) {
229		# Statistic
230	24x	var_row_val <- paste(
231	24x	gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
232	24x	paste(.spl_context$value, collapse = "_"),
233	24x	sep = "_"
234		)
235	24x	if (use_cache) {
236	!	if (is.null(cache_env[[var_row_val]])) cache_env[[var_row_val]] <- s_summary(u, ...)
237	!	x_stats <- cache_env[[var_row_val]]
238		} else {
239	24x	x_stats <- s_summary(u, ...)
240		}
241
242	24x	if (is.null(imp_rule) \|\| !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
243	24x	res <- x_stats[[stat]]
244		} else {
245	!	timept <- as.numeric(gsub(".?([0-9\\.]+).", "\\1", tail(.spl_context$value, 1)))
246	!	res_imp <- imputation_rule(
247	!	.df_row, x_stats, stat,
248	!	imp_rule = imp_rule,
249	!	post = grepl("Predose", tail(.spl_context$value, 1)) \|\| timept > 0,
250	!	avalcat_var = avalcat_var
251		)
252	!	res <- res_imp[["val"]]
253	!	na_str <- res_imp[["na_str"]]
254		}
255
256		# Label check and replacement
257	24x	if (length(row_labels) > 1) {
258	12x	if (!(labelstr %in% names(row_labels))) {
259	!	stop(
260	!	"Replacing the labels in do_summarize_row_groups needs a named vector",
261	!	"that contains the split values. In the current split variable ",
262	!	.spl_context$split[nrow(.spl_context)],
263	!	" the labelstr value (split value by default) ", labelstr, " is not in",
264	!	" row_labels names: ", names(row_labels)
265		)
266		}
267	12x	lbl <- unlist(row_labels[labelstr])
268		} else {
269	12x	lbl <- labelstr
270		}
271
272		# Cell creation
273	24x	rcell(res,
274	24x	label = lbl,
275	24x	format = formats_v[names(formats_v) == stat][[1]],
276	24x	format_na_str = na_str,
277	24x	indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
278	24x	align = .aligns
279		)
280		}
281		},
282	2x	stat = .stats,
283	2x	use_cache = cache,
284	2x	cache_env = replicate(length(.stats), env)
285		)
286
287		# Main call to rtables
288	2x	summarize_row_groups(
289	2x	lyt = lyt,
290	2x	var = unique(vars),
291	2x	cfun = cfun_list,
292	2x	na_str = na_str,
293	2x	extra_args = extra_args
294		)
295		} else {
296		# Function list for analyze_colvars
297	7x	afun_list <- Map(
298	7x	function(stat, use_cache, cache_env) {
299	32x	function(u, .spl_context, .df_row, ...) {
300		# Main statistics
301	210x	var_row_val <- paste(
302	210x	gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
303	210x	paste(.spl_context$value, collapse = "_"),
304	210x	sep = "_"
305		)
306	210x	if (use_cache) {
307	16x	if (is.null(cache_env[[var_row_val]])) cache_env[[var_row_val]] <- s_summary(u, ...)
308	56x	x_stats <- cache_env[[var_row_val]]
309		} else {
310	154x	x_stats <- s_summary(u, ...)
311		}
312
313	210x	if (is.null(imp_rule) \|\| !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
314	170x	res <- x_stats[[stat]]
315		} else {
316	40x	timept <- as.numeric(gsub(".?([0-9\\.]+).", "\\1", tail(.spl_context$value, 1)))
317	40x	res_imp <- imputation_rule(
318	40x	.df_row, x_stats, stat,
319	40x	imp_rule = imp_rule,
320	40x	post = grepl("Predose", tail(.spl_context$value, 1)) \|\| timept > 0,
321	40x	avalcat_var = avalcat_var
322		)
323	40x	res <- res_imp[["val"]]
324	40x	na_str <- res_imp[["na_str"]]
325		}
326
327	210x	if (is.list(res)) {
328	19x	if (length(res) > 1) {
329	1x	stop("The analyzed column produced more than one category of results.")
330		} else {
331	18x	res <- unlist(res)
332		}
333		}
334
335		# Label from context
336	209x	label_from_context <- .spl_context$value[nrow(.spl_context)]
337
338		# Label switcher
339	209x	if (is.null(row_labels)) {
340	149x	lbl <- label_from_context
341		} else {
342	60x	if (length(row_labels) > 1) {
343	48x	if (!(label_from_context %in% names(row_labels))) {
344	!	stop(
345	!	"Replacing the labels in do_summarize_row_groups needs a named vector",
346	!	"that contains the split values. In the current split variable ",
347	!	.spl_context$split[nrow(.spl_context)],
348	!	" the split value ", label_from_context, " is not in",
349	!	" row_labels names: ", names(row_labels)
350		)
351		}
352	48x	lbl <- unlist(row_labels[label_from_context])
353		} else {
354	12x	lbl <- row_labels
355		}
356		}
357
358		# Cell creation
359	209x	rcell(res,
360	209x	label = lbl,
361	209x	format = formats_v[names(formats_v) == stat][[1]],
362	209x	format_na_str = na_str,
363	209x	indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
364	209x	align = .aligns
365		)
366		}
367		},
368	7x	stat = .stats,
369	7x	use_cache = cache,
370	7x	cache_env = replicate(length(.stats), env)
371		)
372
373		# Main call to rtables
374	7x	analyze_colvars(lyt,
375	7x	afun = afun_list,
376	7x	na_str = na_str,
377	7x	nested = nested,
378	7x	extra_args = extra_args
379		)
380		}
381		}
382
383		# Help function
384		get_last_col_split <- function(lyt) {
385	1x	tail(tail(clayout(lyt), 1)[[1]], 1)[[1]]
386		}

1		#' Helper Functions for Tabulating Survival Duration by Subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions that tabulate in a data frame statistics such as median survival
6		#' time and hazard ratio for population subgroups.
7		#'
8		#' @inheritParams argument_convention
9		#' @inheritParams survival_coxph_pairwise
10		#' @inheritParams survival_duration_subgroups
11		#' @param arm (`factor`)\cr the treatment group variable.
12		#'
13		#' @details Main functionality is to prepare data for use in a layout-creating function.
14		#'
15		#' @examples
16		#' library(dplyr)
17		#' library(forcats)
18		#'
19		#' adtte <- tern_ex_adtte
20		#'
21		#' # Save variable labels before data processing steps.
22		#' adtte_labels <- formatters::var_labels(adtte)
23		#'
24		#' adtte_f <- adtte %>%
25		#' filter(
26		#' PARAMCD == "OS",
27		#' ARM %in% c("B: Placebo", "A: Drug X"),
28		#' SEX %in% c("M", "F")
29		#' ) %>%
30		#' mutate(
31		#' # Reorder levels of ARM to display reference arm before treatment arm.
32		#' ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
33		#' SEX = droplevels(SEX),
34		#' is_event = CNSR == 0
35		#' )
36		#' labels <- c("ARM" = adtte_labels[["ARM"]], "SEX" = adtte_labels[["SEX"]], "is_event" = "Event Flag")
37		#' formatters::var_labels(adtte_f)[names(labels)] <- labels
38		#'
39		#' @name h_survival_duration_subgroups
40		NULL
41
42		#' @describeIn h_survival_duration_subgroups helper to prepare a data frame of median survival times by arm.
43		#'
44		#' @return
45		#' * `h_survtime_df()` returns a `data.frame` with columns `arm`, `n`, `n_events`, and `median`.
46		#'
47		#' @examples
48		#' # Extract median survival time for one group.
49		#' h_survtime_df(
50		#' tte = adtte_f$AVAL,
51		#' is_event = adtte_f$is_event,
52		#' arm = adtte_f$ARM
53		#' )
54		#'
55		#' @export
56		h_survtime_df <- function(tte, is_event, arm) {
57	61x	checkmate::assert_numeric(tte)
58	60x	checkmate::assert_logical(is_event, len = length(tte))
59	60x	assert_valid_factor(arm, len = length(tte))
60
61	60x	df_tte <- data.frame(
62	60x	tte = tte,
63	60x	is_event = is_event,
64	60x	stringsAsFactors = FALSE
65		)
66
67		# Delete NAs
68	60x	non_missing_rows <- stats::complete.cases(df_tte)
69	60x	df_tte <- df_tte[non_missing_rows, ]
70	60x	arm <- arm[non_missing_rows]
71
72	60x	lst_tte <- split(df_tte, arm)
73	60x	lst_results <- Map(function(x, arm) {
74	120x	if (nrow(x) > 0) {
75	116x	s_surv <- s_surv_time(x, .var = "tte", is_event = "is_event")
76	116x	median_est <- unname(as.numeric(s_surv$median))
77	116x	n_events <- sum(x$is_event)
78		} else {
79	4x	median_est <- NA
80	4x	n_events <- NA
81		}
82
83	120x	data.frame(
84	120x	arm = arm,
85	120x	n = nrow(x),
86	120x	n_events = n_events,
87	120x	median = median_est,
88	120x	stringsAsFactors = FALSE
89		)
90	60x	}, lst_tte, names(lst_tte))
91
92	60x	df <- do.call(rbind, args = c(lst_results, make.row.names = FALSE))
93	60x	df$arm <- factor(df$arm, levels = levels(arm))
94	60x	df
95		}
96
97		#' @describeIn h_survival_duration_subgroups summarizes median survival times by arm and across subgroups
98		#' in a data frame. `variables` corresponds to the names of variables found in `data`, passed as a named list and
99		#' requires elements `tte`, `is_event`, `arm` and optionally `subgroups`. `groups_lists` optionally specifies
100		#' groupings for `subgroups` variables.
101		#'
102		#' @return
103		#' * `h_survtime_subgroups_df()` returns a `data.frame` with columns `arm`, `n`, `n_events`, `median`, `subgroup`,
104		#' `var`, `var_label`, and `row_type`.
105		#'
106		#' @examples
107		#' # Extract median survival time for multiple groups.
108		#' h_survtime_subgroups_df(
109		#' variables = list(
110		#' tte = "AVAL",
111		#' is_event = "is_event",
112		#' arm = "ARM",
113		#' subgroups = c("SEX", "BMRKR2")
114		#' ),
115		#' data = adtte_f
116		#' )
117		#'
118		#' # Define groupings for BMRKR2 levels.
119		#' h_survtime_subgroups_df(
120		#' variables = list(
121		#' tte = "AVAL",
122		#' is_event = "is_event",
123		#' arm = "ARM",
124		#' subgroups = c("SEX", "BMRKR2")
125		#' ),
126		#' data = adtte_f,
127		#' groups_lists = list(
128		#' BMRKR2 = list(
129		#' "low" = "LOW",
130		#' "low/medium" = c("LOW", "MEDIUM"),
131		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
132		#' )
133		#' )
134		#' )
135		#'
136		#' @export
137		h_survtime_subgroups_df <- function(variables,
138		data,
139		groups_lists = list(),
140		label_all = "All Patients") {
141	12x	checkmate::assert_character(variables$tte)
142	12x	checkmate::assert_character(variables$is_event)
143	12x	checkmate::assert_character(variables$arm)
144	12x	checkmate::assert_character(variables$subgroups, null.ok = TRUE)
145
146	12x	assert_df_with_variables(data, variables)
147
148	12x	checkmate::assert_string(label_all)
149
150		# Add All Patients.
151	12x	result_all <- h_survtime_df(data[[variables$tte]], data[[variables$is_event]], data[[variables$arm]])
152	12x	result_all$subgroup <- label_all
153	12x	result_all$var <- "ALL"
154	12x	result_all$var_label <- label_all
155	12x	result_all$row_type <- "content"
156
157		# Add Subgroups.
158	12x	if (is.null(variables$subgroups)) {
159	3x	result_all
160		} else {
161	9x	l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
162	9x	l_result <- lapply(l_data, function(grp) {
163	45x	result <- h_survtime_df(grp$df[[variables$tte]], grp$df[[variables$is_event]], grp$df[[variables$arm]])
164	45x	result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
165	45x	cbind(result, result_labels)
166		})
167	9x	result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
168	9x	result_subgroups$row_type <- "analysis"
169	9x	rbind(
170	9x	result_all,
171	9x	result_subgroups
172		)
173		}
174		}
175
176		#' @describeIn h_survival_duration_subgroups helper to prepare a data frame with estimates of
177		#' treatment hazard ratio.
178		#'
179		#' @param strata_data (`factor`, `data.frame` or `NULL`)\cr required if stratified analysis is performed.
180		#'
181		#' @return
182		#' * `h_coxph_df()` returns a `data.frame` with columns `arm`, `n_tot`, `n_tot_events`, `hr`, `lcl`, `ucl`,
183		#' `conf_level`, `pval` and `pval_label`.
184		#'
185		#' @examples
186		#' # Extract hazard ratio for one group.
187		#' h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM)
188		#'
189		#' # Extract hazard ratio for one group with stratification factor.
190		#' h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1)
191		#'
192		#' @export
193		h_coxph_df <- function(tte, is_event, arm, strata_data = NULL, control = control_coxph()) {
194	64x	checkmate::assert_numeric(tte)
195	64x	checkmate::assert_logical(is_event, len = length(tte))
196	64x	assert_valid_factor(arm, n.levels = 2, len = length(tte))
197
198	64x	df_tte <- data.frame(tte = tte, is_event = is_event)
199	64x	strata_vars <- NULL
200
201	64x	if (!is.null(strata_data)) {
202	5x	if (is.data.frame(strata_data)) {
203	4x	strata_vars <- names(strata_data)
204	4x	checkmate::assert_data_frame(strata_data, nrows = nrow(df_tte))
205	4x	assert_df_with_factors(strata_data, as.list(stats::setNames(strata_vars, strata_vars)))
206		} else {
207	1x	assert_valid_factor(strata_data, len = nrow(df_tte))
208	1x	strata_vars <- "strata_data"
209		}
210	5x	df_tte[strata_vars] <- strata_data
211		}
212
213	64x	l_df <- split(df_tte, arm)
214
215	64x	if (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) > 0) {
216		# Hazard ratio and CI.
217	60x	result <- s_coxph_pairwise(
218	60x	df = l_df[[2]],
219	60x	.ref_group = l_df[[1]],
220	60x	.in_ref_col = FALSE,
221	60x	.var = "tte",
222	60x	is_event = "is_event",
223	60x	strata = strata_vars,
224	60x	control = control
225		)
226
227	60x	df <- data.frame(
228		# Dummy column needed downstream to create a nested header.
229	60x	arm = " ",
230	60x	n_tot = unname(as.numeric(result$n_tot)),
231	60x	n_tot_events = unname(as.numeric(result$n_tot_events)),
232	60x	hr = unname(as.numeric(result$hr)),
233	60x	lcl = unname(result$hr_ci[1]),
234	60x	ucl = unname(result$hr_ci[2]),
235	60x	conf_level = control[["conf_level"]],
236	60x	pval = as.numeric(result$pvalue),
237	60x	pval_label = obj_label(result$pvalue),
238	60x	stringsAsFactors = FALSE
239		)
240		} else if (
241	4x	(nrow(l_df[[1]]) == 0 && nrow(l_df[[2]]) > 0) \|\|
242	4x	(nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) == 0)
243		) {
244	4x	df_tte_complete <- df_tte[stats::complete.cases(df_tte), ]
245	4x	df <- data.frame(
246		# Dummy column needed downstream to create a nested header.
247	4x	arm = " ",
248	4x	n_tot = nrow(df_tte_complete),
249	4x	n_tot_events = sum(df_tte_complete$is_event),
250	4x	hr = NA,
251	4x	lcl = NA,
252	4x	ucl = NA,
253	4x	conf_level = control[["conf_level"]],
254	4x	pval = NA,
255	4x	pval_label = NA,
256	4x	stringsAsFactors = FALSE
257		)
258		} else {
259	!	df <- data.frame(
260		# Dummy column needed downstream to create a nested header.
261	!	arm = " ",
262	!	n_tot = 0L,
263	!	n_tot_events = 0L,
264	!	hr = NA,
265	!	lcl = NA,
266	!	ucl = NA,
267	!	conf_level = control[["conf_level"]],
268	!	pval = NA,
269	!	pval_label = NA,
270	!	stringsAsFactors = FALSE
271		)
272		}
273
274	64x	df
275		}
276
277		#' @describeIn h_survival_duration_subgroups summarizes estimates of the treatment hazard ratio
278		#' across subgroups in a data frame. `variables` corresponds to the names of variables found in
279		#' `data`, passed as a named list and requires elements `tte`, `is_event`, `arm` and
280		#' optionally `subgroups` and `strata`. `groups_lists` optionally specifies
281		#' groupings for `subgroups` variables.
282		#'
283		#' @return
284		#' * `h_coxph_subgroups_df()` returns a `data.frame` with columns `arm`, `n_tot`, `n_tot_events`, `hr`,
285		#' `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`, `var_label`, and `row_type`.
286		#'
287		#' @examples
288		#' # Extract hazard ratio for multiple groups.
289		#' h_coxph_subgroups_df(
290		#' variables = list(
291		#' tte = "AVAL",
292		#' is_event = "is_event",
293		#' arm = "ARM",
294		#' subgroups = c("SEX", "BMRKR2")
295		#' ),
296		#' data = adtte_f
297		#' )
298		#'
299		#' # Define groupings of BMRKR2 levels.
300		#' h_coxph_subgroups_df(
301		#' variables = list(
302		#' tte = "AVAL",
303		#' is_event = "is_event",
304		#' arm = "ARM",
305		#' subgroups = c("SEX", "BMRKR2")
306		#' ),
307		#' data = adtte_f,
308		#' groups_lists = list(
309		#' BMRKR2 = list(
310		#' "low" = "LOW",
311		#' "low/medium" = c("LOW", "MEDIUM"),
312		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
313		#' )
314		#' )
315		#' )
316		#'
317		#' # Extract hazard ratio for multiple groups with stratification factors.
318		#' h_coxph_subgroups_df(
319		#' variables = list(
320		#' tte = "AVAL",
321		#' is_event = "is_event",
322		#' arm = "ARM",
323		#' subgroups = c("SEX", "BMRKR2"),
324		#' strata = c("STRATA1", "STRATA2")
325		#' ),
326		#' data = adtte_f
327		#' )
328		#'
329		#' @export
330		h_coxph_subgroups_df <- function(variables,
331		data,
332		groups_lists = list(),
333		control = control_coxph(),
334		label_all = "All Patients") {
335	13x	if ("strat" %in% names(variables)) {
336	!	warning(
337	!	"Warning: the `strat` element name of the `variables` list argument to `h_coxph_subgroups_df() ",
338	!	"was deprecated in tern 0.9.3.\n ",
339	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
340		)
341	!	variables[["strata"]] <- variables[["strat"]]
342		}
343
344	13x	checkmate::assert_character(variables$tte)
345	13x	checkmate::assert_character(variables$is_event)
346	13x	checkmate::assert_character(variables$arm)
347	13x	checkmate::assert_character(variables$subgroups, null.ok = TRUE)
348	13x	checkmate::assert_character(variables$strata, null.ok = TRUE)
349	13x	assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
350	13x	assert_df_with_variables(data, variables)
351	13x	checkmate::assert_string(label_all)
352
353		# Add All Patients.
354	13x	result_all <- h_coxph_df(
355	13x	tte = data[[variables$tte]],
356	13x	is_event = data[[variables$is_event]],
357	13x	arm = data[[variables$arm]],
358	13x	strata_data = if (is.null(variables$strata)) NULL else data[variables$strata],
359	13x	control = control
360		)
361	13x	result_all$subgroup <- label_all
362	13x	result_all$var <- "ALL"
363	13x	result_all$var_label <- label_all
364	13x	result_all$row_type <- "content"
365
366		# Add Subgroups.
367	13x	if (is.null(variables$subgroups)) {
368	3x	result_all
369		} else {
370	10x	l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
371
372	10x	l_result <- lapply(l_data, function(grp) {
373	47x	result <- h_coxph_df(
374	47x	tte = grp$df[[variables$tte]],
375	47x	is_event = grp$df[[variables$is_event]],
376	47x	arm = grp$df[[variables$arm]],
377	47x	strata_data = if (is.null(variables$strata)) NULL else grp$df[variables$strata],
378	47x	control = control
379		)
380	47x	result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
381	47x	cbind(result, result_labels)
382		})
383
384	10x	result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
385	10x	result_subgroups$row_type <- "analysis"
386
387	10x	rbind(
388	10x	result_all,
389	10x	result_subgroups
390		)
391		}
392		}
393
394		#' Split Dataframe by Subgroups
395		#'
396		#' @description `r lifecycle::badge("stable")`
397		#'
398		#' Split a dataframe into a non-nested list of subsets.
399		#'
400		#' @inheritParams argument_convention
401		#' @inheritParams survival_duration_subgroups
402		#' @param data (`data.frame`)\cr dataset to split.
403		#' @param subgroups (`character`)\cr names of factor variables from `data` used to create subsets.
404		#' Unused levels not present in `data` are dropped. Note that the order in this vector
405		#' determines the order in the downstream table.
406		#'
407		#' @return A list with subset data (`df`) and metadata about the subset (`df_labels`).
408		#'
409		#' @details Main functionality is to prepare data for use in forest plot layouts.
410		#'
411		#' @examples
412		#' df <- data.frame(
413		#' x = c(1:5),
414		#' y = factor(c("A", "B", "A", "B", "A"), levels = c("A", "B", "C")),
415		#' z = factor(c("C", "C", "D", "D", "D"), levels = c("D", "C"))
416		#' )
417		#' formatters::var_labels(df) <- paste("label for", names(df))
418		#'
419		#' h_split_by_subgroups(
420		#' data = df,
421		#' subgroups = c("y", "z")
422		#' )
423		#'
424		#' h_split_by_subgroups(
425		#' data = df,
426		#' subgroups = c("y", "z"),
427		#' groups_lists = list(
428		#' y = list("AB" = c("A", "B"), "C" = "C")
429		#' )
430		#' )
431		#'
432		#' @export
433		h_split_by_subgroups <- function(data,
434		subgroups,
435		groups_lists = list()) {
436	52x	checkmate::assert_character(subgroups, min.len = 1, any.missing = FALSE)
437	52x	checkmate::assert_list(groups_lists, names = "named")
438	52x	checkmate::assert_subset(names(groups_lists), subgroups)
439	52x	assert_df_with_factors(data, as.list(stats::setNames(subgroups, subgroups)))
440
441	52x	data_labels <- unname(formatters::var_labels(data))
442	52x	df_subgroups <- data[, subgroups, drop = FALSE]
443	52x	subgroup_labels <- formatters::var_labels(df_subgroups, fill = TRUE)
444
445	52x	l_labels <- Map(function(grp_i, name_i) {
446	93x	existing_levels <- levels(droplevels(grp_i))
447	93x	grp_levels <- if (name_i %in% names(groups_lists)) {
448		# For this variable groupings are defined. We check which groups are contained in the data.
449	11x	group_list_i <- groups_lists[[name_i]]
450	11x	group_has_levels <- vapply(group_list_i, function(lvls) any(lvls %in% existing_levels), TRUE)
451	11x	names(which(group_has_levels))
452		} else {
453	82x	existing_levels
454		}
455	93x	df_labels <- data.frame(
456	93x	subgroup = grp_levels,
457	93x	var = name_i,
458	93x	var_label = unname(subgroup_labels[name_i]),
459	93x	stringsAsFactors = FALSE # Rationale is that subgroups may not be unique.
460		)
461	52x	}, df_subgroups, names(df_subgroups))
462
463		# Create a dataframe with one row per subgroup.
464	52x	df_labels <- do.call(rbind, args = c(l_labels, make.row.names = FALSE))
465	52x	row_label <- paste0(df_labels$var, ".", df_labels$subgroup)
466	52x	row_split_var <- factor(row_label, levels = row_label)
467
468		# Create a list of data subsets.
469	52x	lapply(split(df_labels, row_split_var), function(row_i) {
470	233x	which_row <- if (row_i$var %in% names(groups_lists)) {
471	31x	data[[row_i$var]] %in% groups_lists[[row_i$var]][[row_i$subgroup]]
472		} else {
473	202x	data[[row_i$var]] == row_i$subgroup
474		}
475	233x	df <- data[which_row, ]
476	233x	rownames(df) <- NULL
477	233x	formatters::var_labels(df) <- data_labels
478
479	233x	list(
480	233x	df = df,
481	233x	df_labels = data.frame(row_i, row.names = NULL)
482		)
483		})
484		}

1		#' Helper Function to create a map dataframe that can be used in `trim_levels_to_map` split function.
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper Function to create a map dataframe from the input dataset, which can be used as an argument in the
6		#' `trim_levels_to_map` split function. Based on different method, the map is constructed differently.
7		#'
8		#' @inheritParams argument_convention
9		#' @param abnormal (named `list`)\cr identifying the abnormal range level(s) in `df`. Based on the levels of
10		#' abnormality of the input dataset, it can be something like `list(Low = "LOW LOW", High = "HIGH HIGH")` or
11		#' `abnormal = list(Low = "LOW", High = "HIGH"))`
12		#' @param method (`string`)\cr indicates how the returned map will be constructed. Can be `"default"` or `"range"`.
13		#'
14		#' @return A map `data.frame`.
15		#'
16		#' @note If method is `"default"`, the returned map will only have the abnormal directions that are observed in the
17		#' `df`, and records with all normal values will be excluded to avoid error in creating layout. If method is
18		#' `"range"`, the returned map will be based on the rule that at least one observation with low range > 0
19		#' for low direction and at least one observation with high range is not missing for high direction.
20		#'
21		#' @examples
22		#' adlb <- df_explicit_na(tern_ex_adlb)
23		#'
24		#' h_map_for_count_abnormal(
25		#' df = adlb,
26		#' variables = list(anl = "ANRIND", split_rows = c("LBCAT", "PARAM")),
27		#' abnormal = list(low = c("LOW"), high = c("HIGH")),
28		#' method = "default",
29		#' na_str = "<Missing>"
30		#' )
31		#'
32		#' df <- data.frame(
33		#' USUBJID = c(rep("1", 4), rep("2", 4), rep("3", 4)),
34		#' AVISIT = c(
35		#' rep("WEEK 1", 2),
36		#' rep("WEEK 2", 2),
37		#' rep("WEEK 1", 2),
38		#' rep("WEEK 2", 2),
39		#' rep("WEEK 1", 2),
40		#' rep("WEEK 2", 2)
41		#' ),
42		#' PARAM = rep(c("ALT", "CPR"), 6),
43		#' ANRIND = c(
44		#' "NORMAL", "NORMAL", "LOW",
45		#' "HIGH", "LOW", "LOW", "HIGH", "HIGH", rep("NORMAL", 4)
46		#' ),
47		#' ANRLO = rep(5, 12),
48		#' ANRHI = rep(20, 12)
49		#' )
50		#' df$ANRIND <- factor(df$ANRIND, levels = c("LOW", "HIGH", "NORMAL"))
51		#' h_map_for_count_abnormal(
52		#' df = df,
53		#' variables = list(
54		#' anl = "ANRIND",
55		#' split_rows = c("PARAM"),
56		#' range_low = "ANRLO",
57		#' range_high = "ANRHI"
58		#' ),
59		#' abnormal = list(low = c("LOW"), high = c("HIGH")),
60		#' method = "range",
61		#' na_str = "<Missing>"
62		#' )
63		#'
64		#' @export
65		h_map_for_count_abnormal <- function(df,
66		variables = list(
67		anl = "ANRIND",
68		split_rows = c("PARAM"),
69		range_low = "ANRLO",
70		range_high = "ANRHI"
71		),
72		abnormal = list(low = c("LOW", "LOW LOW"), high = c("HIGH", "HIGH HIGH")),
73		method = c("default", "range"),
74		na_str = "<Missing>") {
75	7x	method <- match.arg(method)
76	7x	checkmate::assert_subset(c("anl", "split_rows"), names(variables))
77	7x	checkmate::assert_false(anyNA(df[variables$split_rows]))
78	7x	assert_df_with_variables(df,
79	7x	variables = list(anl = variables$anl, split_rows = variables$split_rows),
80	7x	na_level = na_str
81		)
82	7x	assert_df_with_factors(df, list(val = variables$anl))
83	7x	assert_valid_factor(df[[variables$anl]], any.missing = FALSE)
84	7x	assert_list_of_variables(variables)
85	7x	checkmate::assert_list(abnormal, types = "character", len = 2)
86
87		# Drop usued levels from df as they are not supposed to be in the final map
88	7x	df <- droplevels(df)
89
90	7x	normal_value <- setdiff(levels(df[[variables$anl]]), unlist(abnormal))
91
92		# Based on the understanding of clinical data, there should only be one level of normal which is "NORMAL"
93	7x	checkmate::assert_vector(normal_value, len = 1)
94
95		# Default method will only have what is observed in the df, and records with all normal values will be excluded to
96		# avoid error in layout building.
97	7x	if (method == "default") {
98	3x	df_abnormal <- subset(df, df[[variables$anl]] %in% unlist(abnormal))
99	3x	map <- unique(df_abnormal[c(variables$split_rows, variables$anl)])
100	3x	map_normal <- unique(subset(map, select = variables$split_rows))
101	3x	map_normal[[variables$anl]] <- normal_value
102	3x	map <- rbind(map, map_normal)
103	4x	} else if (method == "range") {
104		# range method follows the rule that at least one observation with ANRLO > 0 for low
105		# direction and at least one observation with ANRHI is not missing for high direction.
106	4x	checkmate::assert_subset(c("range_low", "range_high"), names(variables))
107	4x	checkmate::assert_subset(c("LOW", "HIGH"), toupper(names(abnormal)))
108
109	4x	assert_df_with_variables(df,
110	4x	variables = list(
111	4x	range_low = variables$range_low,
112	4x	range_high = variables$range_high
113		)
114		)
115
116		# Define low direction of map
117	4x	df_low <- subset(df, df[[variables$range_low]] > 0)
118	4x	map_low <- unique(df_low[variables$split_rows])
119	4x	low_levels <- unname(unlist(abnormal[toupper(names(abnormal)) == "LOW"]))
120	4x	low_levels_df <- as.data.frame(low_levels)
121	4x	colnames(low_levels_df) <- variables$anl
122	4x	low_levels_df <- do.call("rbind", replicate(nrow(map_low), low_levels_df, simplify = FALSE))
123	4x	rownames(map_low) <- NULL # Just to avoid strange row index in case upstream functions changed
124	4x	map_low <- map_low[rep(seq_len(nrow(map_low)), each = length(low_levels)), , drop = FALSE]
125	4x	map_low <- cbind(map_low, low_levels_df)
126
127		# Define high direction of map
128	4x	df_high <- subset(df, df[[variables$range_high]] != na_str \| !is.na(df[[variables$range_high]]))
129	4x	map_high <- unique(df_high[variables$split_rows])
130	4x	high_levels <- unname(unlist(abnormal[toupper(names(abnormal)) == "HIGH"]))
131	4x	high_levels_df <- as.data.frame(high_levels)
132	4x	colnames(high_levels_df) <- variables$anl
133	4x	high_levels_df <- do.call("rbind", replicate(nrow(map_high), high_levels_df, simplify = FALSE))
134	4x	rownames(map_high) <- NULL
135	4x	map_high <- map_high[rep(seq_len(nrow(map_high)), each = length(high_levels)), , drop = FALSE]
136	4x	map_high <- cbind(map_high, high_levels_df)
137
138		# Define normal of map
139	4x	map_normal <- unique(rbind(map_low, map_high)[variables$split_rows])
140	4x	map_normal[variables$anl] <- normal_value
141
142	4x	map <- rbind(map_low, map_high, map_normal)
143		}
144
145		# map should be all characters
146	7x	map <- data.frame(lapply(map, as.character), stringsAsFactors = FALSE)
147
148		# sort the map final output by split_rows variables
149	7x	for (i in rev(seq_len(length(variables$split_rows)))) {
150	7x	map <- map[order(map[[i]]), ]
151		}
152	7x	map
153		}

1		#' Encode Categorical Missing Values in a Data Frame
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' This is a helper function to encode missing entries across groups of categorical
6		#' variables in a data frame.
7		#'
8		#' @details Missing entries are those with `NA` or empty strings and will
9		#' be replaced with a specified value. If factor variables include missing
10		#' values, the missing value will be inserted as the last level.
11		#' Similarly, in case character or logical variables should be converted to factors
12		#' with the `char_as_factor` or `logical_as_factor` options, the missing values will
13		#' be set as the last level.
14		#'
15		#' @param data (`data.frame`)\cr data set.
16		#' @param omit_columns (`character`)\cr names of variables from `data` that should
17		#' not be modified by this function.
18		#' @param char_as_factor (`flag`)\cr whether to convert character variables
19		#' in `data` to factors.
20		#' @param logical_as_factor (`flag`)\cr whether to convert logical variables
21		#' in `data` to factors.
22		#' @param na_level (`string`)\cr used to replace all `NA` or empty
23		#' values inside non-`omit_columns` columns.
24		#'
25		#' @return A `data.frame` with the chosen modifications applied.
26		#'
27		#' @seealso [sas_na()] and [explicit_na()] for other missing data helper functions.
28		#'
29		#' @examples
30		#' my_data <- data.frame(
31		#' u = c(TRUE, FALSE, NA, TRUE),
32		#' v = factor(c("A", NA, NA, NA), levels = c("Z", "A")),
33		#' w = c("A", "B", NA, "C"),
34		#' x = c("D", "E", "F", NA),
35		#' y = c("G", "H", "I", ""),
36		#' z = c(1, 2, 3, 4),
37		#' stringsAsFactors = FALSE
38		#' )
39		#'
40		#' # Example 1
41		#' # Encode missing values in all character or factor columns.
42		#' df_explicit_na(my_data)
43		#' # Also convert logical columns to factor columns.
44		#' df_explicit_na(my_data, logical_as_factor = TRUE)
45		#' # Encode missing values in a subset of columns.
46		#' df_explicit_na(my_data, omit_columns = c("x", "y"))
47		#'
48		#' # Example 2
49		#' # Here we purposefully convert all `M` values to `NA` in the `SEX` variable.
50		#' # After running `df_explicit_na` the `NA` values are encoded as `<Missing>` but they are not
51		#' # included when generating `rtables`.
52		#' adsl <- tern_ex_adsl
53		#' adsl$SEX[adsl$SEX == "M"] <- NA
54		#' adsl <- df_explicit_na(adsl)
55		#'
56		#' # If you want the `Na` values to be displayed in the table use the `na_level` argument.
57		#' adsl <- tern_ex_adsl
58		#' adsl$SEX[adsl$SEX == "M"] <- NA
59		#' adsl <- df_explicit_na(adsl, na_level = "Missing Values")
60		#'
61		#' # Example 3
62		#' # Numeric variables that have missing values are not altered. This means that any `NA` value in
63		#' # a numeric variable will not be included in the summary statistics, nor will they be included
64		#' # in the denominator value for calculating the percent values.
65		#' adsl <- tern_ex_adsl
66		#' adsl$AGE[adsl$AGE < 30] <- NA
67		#' adsl <- df_explicit_na(adsl)
68		#'
69		#' @export
70		df_explicit_na <- function(data,
71		omit_columns = NULL,
72		char_as_factor = TRUE,
73		logical_as_factor = FALSE,
74		na_level = "<Missing>") {
75	21x	checkmate::assert_character(omit_columns, null.ok = TRUE, min.len = 1, any.missing = FALSE)
76	20x	checkmate::assert_data_frame(data)
77	19x	checkmate::assert_flag(char_as_factor)
78	18x	checkmate::assert_flag(logical_as_factor)
79	18x	checkmate::assert_string(na_level)
80
81	16x	target_vars <- if (is.null(omit_columns)) {
82	14x	names(data)
83		} else {
84	2x	setdiff(names(data), omit_columns) # May have duplicates.
85		}
86	16x	if (length(target_vars) == 0) {
87	1x	return(data)
88		}
89
90	15x	l_target_vars <- split(target_vars, target_vars)
91
92		# Makes sure target_vars exist in data and names are not duplicated.
93	15x	assert_df_with_variables(data, l_target_vars)
94
95	15x	for (x in target_vars) {
96	276x	xi <- data[[x]]
97	276x	xi_label <- obj_label(xi)
98
99		# Determine whether to convert character or logical input.
100	276x	do_char_conversion <- is.character(xi) && char_as_factor
101	276x	do_logical_conversion <- is.logical(xi) && logical_as_factor
102
103		# Pre-convert logical to character to deal correctly with replacing NA
104		# values below.
105	276x	if (do_logical_conversion) {
106	2x	xi <- as.character(xi)
107		}
108
109	276x	if (is.factor(xi) \|\| is.character(xi)) {
110		# Handle empty strings and NA values.
111	198x	xi <- explicit_na(sas_na(xi), label = na_level)
112
113		# Convert to factors if requested for the original type,
114		# set na_level as the last value.
115	198x	if (do_char_conversion \|\| do_logical_conversion) {
116	71x	levels_xi <- setdiff(sort(unique(xi)), na_level)
117	71x	if (na_level %in% unique(xi)) {
118	18x	levels_xi <- c(levels_xi, na_level)
119		}
120
121	71x	xi <- factor(xi, levels = levels_xi)
122		}
123
124	198x	data[, x] <- formatters::with_label(xi, label = xi_label)
125		}
126		}
127	15x	return(data)
128		}

1		#' Helper Functions for Cox Proportional Hazards Regression
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions used in [fit_coxreg_univar()] and [fit_coxreg_multivar()].
6		#'
7		#' @inheritParams argument_convention
8		#' @inheritParams h_coxreg_univar_extract
9		#' @inheritParams cox_regression_inter
10		#' @inheritParams control_coxreg
11		#'
12		#' @seealso [cox_regression]
13		#'
14		#' @name h_cox_regression
15		NULL
16
17		#' @describeIn h_cox_regression Helper for Cox regression formula. Creates a list of formulas. It is used
18		#' internally by [fit_coxreg_univar()] for the comparison of univariate Cox regression models.
19		#'
20		#' @return
21		#' * `h_coxreg_univar_formulas()` returns a `character` vector coercible into formulas (e.g [stats::as.formula()]).
22		#'
23		#' @examples
24		#' # `h_coxreg_univar_formulas`
25		#'
26		#' ## Simple formulas.
27		#' h_coxreg_univar_formulas(
28		#' variables = list(
29		#' time = "time", event = "status", arm = "armcd", covariates = c("X", "y")
30		#' )
31		#' )
32		#'
33		#' ## Addition of an optional strata.
34		#' h_coxreg_univar_formulas(
35		#' variables = list(
36		#' time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
37		#' strata = "SITE"
38		#' )
39		#' )
40		#'
41		#' ## Inclusion of the interaction term.
42		#' h_coxreg_univar_formulas(
43		#' variables = list(
44		#' time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
45		#' strata = "SITE"
46		#' ),
47		#' interaction = TRUE
48		#' )
49		#'
50		#' ## Only covariates fitted in separate models.
51		#' h_coxreg_univar_formulas(
52		#' variables = list(
53		#' time = "time", event = "status", covariates = c("X", "y")
54		#' )
55		#' )
56		#'
57		#' @export
58		h_coxreg_univar_formulas <- function(variables,
59		interaction = FALSE) {
60	41x	checkmate::assert_list(variables, names = "named")
61	41x	has_arm <- "arm" %in% names(variables)
62	41x	arm_name <- if (has_arm) "arm" else NULL
63
64	41x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
65
66	41x	checkmate::assert_flag(interaction)
67
68	41x	if (!has_arm \|\| is.null(variables$covariates)) {
69	10x	checkmate::assert_false(interaction)
70		}
71
72	39x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
73
74	39x	if (!is.null(variables$covariates)) {
75	38x	forms <- paste0(
76	38x	"survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
77	38x	ifelse(has_arm, variables$arm, "1"),
78	38x	ifelse(interaction, " * ", " + "),
79	38x	variables$covariates,
80	38x	ifelse(
81	38x	!is.null(variables$strata),
82	38x	paste0(" + strata(", paste0(variables$strata, collapse = ", "), ")"),
83		""
84		)
85		)
86		} else {
87	1x	forms <- NULL
88		}
89	39x	nams <- variables$covariates
90	39x	if (has_arm) {
91	32x	ref <- paste0(
92	32x	"survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
93	32x	variables$arm,
94	32x	ifelse(
95	32x	!is.null(variables$strata),
96	32x	paste0(
97	32x	" + strata(", paste0(variables$strata, collapse = ", "), ")"
98		),
99		""
100		)
101		)
102	32x	forms <- c(ref, forms)
103	32x	nams <- c("ref", nams)
104		}
105	39x	stats::setNames(forms, nams)
106		}
107
108		#' @describeIn h_cox_regression Helper for multivariate Cox regression formula. Creates a formulas
109		#' string. It is used internally by [fit_coxreg_multivar()] for the comparison of multivariate Cox
110		#' regression models. Interactions will not be included in multivariate Cox regression model.
111		#'
112		#' @return
113		#' * `h_coxreg_multivar_formula()` returns a `string` coercible into a formula (e.g [stats::as.formula()]).
114		#'
115		#' @examples
116		#' # `h_coxreg_multivar_formula`
117		#'
118		#' h_coxreg_multivar_formula(
119		#' variables = list(
120		#' time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE")
121		#' )
122		#' )
123		#'
124		#' # Addition of an optional strata.
125		#' h_coxreg_multivar_formula(
126		#' variables = list(
127		#' time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE"),
128		#' strata = "SITE"
129		#' )
130		#' )
131		#'
132		#' # Example without treatment arm.
133		#' h_coxreg_multivar_formula(
134		#' variables = list(
135		#' time = "AVAL", event = "event", covariates = c("RACE", "AGE"),
136		#' strata = "SITE"
137		#' )
138		#' )
139		#'
140		#' @export
141		h_coxreg_multivar_formula <- function(variables) {
142	69x	checkmate::assert_list(variables, names = "named")
143	69x	has_arm <- "arm" %in% names(variables)
144	69x	arm_name <- if (has_arm) "arm" else NULL
145
146	69x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
147
148	69x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
149
150	69x	y <- paste0(
151	69x	"survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
152	69x	ifelse(has_arm, variables$arm, "1")
153		)
154	69x	if (length(variables$covariates) > 0) {
155	18x	y <- paste(y, paste(variables$covariates, collapse = " + "), sep = " + ")
156		}
157	69x	if (!is.null(variables$strata)) {
158	5x	y <- paste0(y, " + strata(", paste0(variables$strata, collapse = ", "), ")")
159		}
160	69x	y
161		}
162
163		#' @describeIn h_cox_regression Utility function to help tabulate the result of
164		#' a univariate Cox regression model.
165		#'
166		#' @param effect (`string`)\cr the treatment variable.
167		#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
168		#'
169		#' @return
170		#' * `h_coxreg_univar_extract()` returns a `data.frame` with variables `effect`, `term`, `term_label`, `level`,
171		#' `n`, `hr`, `lcl`, `ucl`, and `pval`.
172		#'
173		#' @examples
174		#' library(survival)
175		#'
176		#' dta_simple <- data.frame(
177		#' time = c(5, 5, 10, 10, 5, 5, 10, 10),
178		#' status = c(0, 0, 1, 0, 0, 1, 1, 1),
179		#' armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c("A", "B")),
180		#' var1 = c(45, 55, 65, 75, 55, 65, 85, 75),
181		#' var2 = c("F", "M", "F", "M", "F", "M", "F", "U")
182		#' )
183		#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
184		#' result <- h_coxreg_univar_extract(
185		#' effect = "armcd", covar = "armcd", mod = mod, data = dta_simple
186		#' )
187		#' result
188		#'
189		#' @export
190		h_coxreg_univar_extract <- function(effect,
191		covar,
192		data,
193		mod,
194		control = control_coxreg()) {
195	47x	checkmate::assert_string(covar)
196	47x	checkmate::assert_string(effect)
197	47x	checkmate::assert_class(mod, "coxph")
198	47x	test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
199
200	47x	mod_aov <- muffled_car_anova(mod, test_statistic)
201	47x	msum <- summary(mod, conf.int = control$conf_level)
202	47x	sum_cox <- broom::tidy(msum)
203
204		# Combine results together.
205	47x	effect_aov <- mod_aov[effect, , drop = TRUE]
206	47x	pval <- effect_aov[[grep(pattern = "Pr", x = names(effect_aov)), drop = TRUE]]
207	47x	sum_main <- sum_cox[grepl(effect, sum_cox$level), ]
208
209	47x	term_label <- if (effect == covar) {
210	25x	paste0(
211	25x	levels(data[[covar]])[2],
212	25x	" vs control (",
213	25x	levels(data[[covar]])[1],
214		")"
215		)
216		} else {
217	22x	unname(labels_or_names(data[covar]))
218		}
219	47x	data.frame(
220	47x	effect = ifelse(covar == effect, "Treatment:", "Covariate:"),
221	47x	term = covar,
222	47x	term_label = term_label,
223	47x	level = levels(data[[effect]])[2],
224	47x	n = mod[["n"]],
225	47x	hr = unname(sum_main["exp(coef)"]),
226	47x	lcl = unname(sum_main[grep("lower", names(sum_main))]),
227	47x	ucl = unname(sum_main[grep("upper", names(sum_main))]),
228	47x	pval = pval,
229	47x	stringsAsFactors = FALSE
230		)
231		}
232
233		#' @describeIn h_cox_regression Tabulation of multivariate Cox regressions. Utility function to help
234		#' tabulate the result of a multivariate Cox regression model for a treatment/covariate variable.
235		#'
236		#' @return
237		#' * `h_coxreg_multivar_extract()` returns a `data.frame` with variables `pval`, `hr`, `lcl`, `ucl`, `level`,
238		#' `n`, `term`, and `term_label`.
239		#'
240		#' @examples
241		#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
242		#' result <- h_coxreg_multivar_extract(
243		#' var = "var1", mod = mod, data = dta_simple
244		#' )
245		#' result
246		#'
247		#' @export
248		h_coxreg_multivar_extract <- function(var,
249		data,
250		mod,
251		control = control_coxreg()) {
252	88x	test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
253	88x	mod_aov <- muffled_car_anova(mod, test_statistic)
254
255	88x	msum <- summary(mod, conf.int = control$conf_level)
256	88x	sum_anova <- broom::tidy(mod_aov)
257	88x	sum_cox <- broom::tidy(msum)
258
259	88x	ret_anova <- sum_anova[sum_anova$term == var, c("term", "p.value")]
260	88x	names(ret_anova)[2] <- "pval"
261	88x	if (is.factor(data[[var]])) {
262	29x	ret_cox <- sum_cox[startsWith(prefix = var, x = sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
263		} else {
264	59x	ret_cox <- sum_cox[(var == sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
265		}
266	88x	names(ret_cox)[1:4] <- c("pval", "hr", "lcl", "ucl")
267	88x	varlab <- unname(labels_or_names(data[var]))
268	88x	ret_cox$term <- varlab
269
270	88x	if (is.numeric(data[[var]])) {
271	59x	ret <- ret_cox
272	59x	ret$term_label <- ret$term
273	29x	} else if (length(levels(data[[var]])) <= 2) {
274	18x	ret_anova$pval <- NA
275	18x	ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
276	18x	ret_cox$level <- gsub(var, "", ret_cox$level)
277	18x	ret_cox$term_label <- ret_cox$level
278	18x	ret <- dplyr::bind_rows(ret_anova, ret_cox)
279		} else {
280	11x	ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
281	11x	ret_cox$level <- gsub(var, "", ret_cox$level)
282	11x	ret_cox$term_label <- ret_cox$level
283	11x	ret <- dplyr::bind_rows(ret_anova, ret_cox)
284		}
285
286	88x	as.data.frame(ret)
287		}

1		#' Summary for Poisson Negative Binomial.
2		#'
3		#' @description `r lifecycle::badge("experimental")`
4		#'
5		#' Summarize results of a Poisson Negative Binomial Regression.
6		#' This can be used to analyze count and/or frequency data using a linear model.
7		#'
8		#' @inheritParams h_glm_count
9		#' @inheritParams argument_convention
10		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("summarize_glm_count")`
11		#' to see available statistics for this function.
12		#'
13		#' @name summarize_glm_count
14		#' @order 1
15		NULL
16
17		#' Helper Functions for Poisson Models.
18		#'
19		#' @description `r lifecycle::badge("experimental")`
20		#'
21		#' Helper functions that can be used to return the results of various Poisson models.
22		#'
23		#' @inheritParams argument_convention
24		#'
25		#' @seealso [summarize_glm_count]
26		#'
27		#' @name h_glm_count
28		NULL
29
30		#' @describeIn h_glm_count Helper function to return results of a poisson model.
31		#'
32		#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called
33		#' in `.var` and `variables`.
34		#' @param variables (named `list` of `strings`)\cr list of additional analysis variables, with
35		#' expected elements:
36		#' * `arm` (`string`)\cr group variable, for which the covariate adjusted means of multiple
37		#' groups will be summarized. Specifically, the first level of `arm` variable is taken as the
38		#' reference group.
39		#' * `covariates` (`character`)\cr a vector that can contain single variable names (such as
40		#' `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
41		#' * `offset` (`numeric`)\cr a numeric vector or scalar adding an offset.
42		#' @param weights (`character`)\cr a character vector specifying weights used
43		#' in averaging predictions. Number of weights must equal the number of levels included in the covariates.
44		#' Weights option passed to [emmeans::emmeans()].
45		#'
46		#' @return
47		#' * `h_glm_poisson()` returns the results of a Poisson model.
48		#'
49		#' @keywords internal
50		h_glm_poisson <- function(.var,
51		.df_row,
52		variables,
53		weights) {
54	17x	arm <- variables$arm
55	17x	covariates <- variables$covariates
56	17x	offset <- .df_row[[variables$offset]]
57
58	15x	formula <- stats::as.formula(paste0(
59	15x	.var, " ~ ",
60		" + ",
61	15x	paste(covariates, collapse = " + "),
62		" + ",
63	15x	arm
64		))
65
66	15x	glm_fit <- stats::glm(
67	15x	formula = formula,
68	15x	offset = offset,
69	15x	data = .df_row,
70	15x	family = stats::poisson(link = "log")
71		)
72
73	15x	emmeans_fit <- emmeans::emmeans(
74	15x	glm_fit,
75	15x	specs = arm,
76	15x	data = .df_row,
77	15x	type = "response",
78	15x	offset = 0,
79	15x	weights = weights
80		)
81
82	15x	list(
83	15x	glm_fit = glm_fit,
84	15x	emmeans_fit = emmeans_fit
85		)
86		}
87
88		#' @describeIn h_glm_count Helper function to return results of a quasipoisson model.
89		#'
90		#' @inheritParams summarize_glm_count
91		#'
92		#' @return
93		#' * `h_glm_quasipoisson()` returns the results of a Quasi-Poisson model.
94		#'
95		#' @keywords internal
96		h_glm_quasipoisson <- function(.var,
97		.df_row,
98		variables,
99		weights) {
100	4x	arm <- variables$arm
101	4x	covariates <- variables$covariates
102	4x	offset <- .df_row[[variables$offset]]
103
104	2x	formula <- stats::as.formula(paste0(
105	2x	.var, " ~ ",
106		" + ",
107	2x	paste(covariates, collapse = " + "),
108		" + ",
109	2x	arm
110		))
111
112	2x	glm_fit <- stats::glm(
113	2x	formula = formula,
114	2x	offset = offset,
115	2x	data = .df_row,
116	2x	family = stats::quasipoisson(link = "log")
117		)
118
119	2x	emmeans_fit <- emmeans::emmeans(
120	2x	glm_fit,
121	2x	specs = arm,
122	2x	data = .df_row,
123	2x	type = "response",
124	2x	offset = 0,
125	2x	weights = weights
126		)
127
128	2x	list(
129	2x	glm_fit = glm_fit,
130	2x	emmeans_fit = emmeans_fit
131		)
132		}
133
134		#' @describeIn h_glm_count Helper function to return results of a negative binomial model.
135		#'
136		#' @inheritParams summarize_glm_count
137		#'
138		#' @return
139		#' * `h_glm_negbin()` returns the results of a Negative Binomial model.
140		#'
141		#' @keywords internal
142		h_glm_negbin <- function(.var,
143		.df_row,
144		variables,
145		weights) {
146	9x	arm <- variables$arm
147	9x	covariates <- variables$covariates
148
149	9x	formula <- stats::as.formula(paste0(
150	9x	.var, " ~ ",
151		" + ",
152	9x	paste(covariates, collapse = " + "),
153		" + ",
154	9x	arm
155		))
156
157	9x	glm_fit <- MASS::glm.nb(
158	9x	formula = formula,
159	9x	data = .df_row,
160	9x	link = "log"
161		)
162
163	7x	emmeans_fit <- emmeans::emmeans(
164	7x	glm_fit,
165	7x	specs = arm,
166	7x	data = .df_row,
167	7x	type = "response",
168	7x	offset = 0,
169	7x	weights = weights
170		)
171
172	7x	list(
173	7x	glm_fit = glm_fit,
174	7x	emmeans_fit = emmeans_fit
175		)
176		}
177
178		#' @describeIn h_glm_count Helper function to return the results of the
179		#' selected model (poisson, quasipoisson, negative binomial).
180		#'
181		#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called
182		#' in `.var` and `variables`.
183		#' @param variables (named `list` of `strings`)\cr list of additional analysis variables, with
184		#' expected elements:
185		#' * `arm` (`string`)\cr group variable, for which the covariate adjusted means of multiple
186		#' groups will be summarized. Specifically, the first level of `arm` variable is taken as the
187		#' reference group.
188		#' * `covariates` (`character`)\cr a vector that can contain single variable names (such as
189		#' `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
190		#' * `offset` (`numeric`)\cr a numeric vector or scalar adding an offset.
191		#' @param distribution (`character`)\cr a character value specifying the distribution
192		#' used in the regression (poisson, quasipoisson, negative binomial).
193		#'
194		#' @return
195		#' * `h_glm_count()` returns the results of the selected model.
196		#'
197		#' @keywords internal
198		h_glm_count <- function(.var,
199		.df_row,
200		variables,
201		distribution,
202		weights) {
203	20x	checkmate::assert_subset(distribution, c("poisson", "quasipoisson", "negbin"), empty.ok = FALSE)
204	18x	switch(distribution,
205	13x	poisson = h_glm_poisson(.var, .df_row, variables, weights),
206	!	quasipoisson = h_glm_quasipoisson(.var, .df_row, variables, weights),
207	5x	negbin = h_glm_negbin(.var, .df_row, variables, weights)
208		)
209		}
210
211		#' @describeIn h_glm_count Helper function to return the estimated means.
212		#'
213		#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called in `.var` and `variables`.
214		#' @param conf_level (`numeric`)\cr value used to derive the confidence interval for the rate.
215		#' @param obj (`glm.fit`)\cr fitted model object used to derive the mean rate estimates in each treatment arm.
216		#' @param arm (`string`)\cr group variable, for which the covariate adjusted means of multiple groups will be
217		#' summarized. Specifically, the first level of `arm` variable is taken as the reference group.
218		#'
219		#' @return
220		#' * `h_ppmeans()` returns the estimated means.
221		#'
222		#' @keywords internal
223		h_ppmeans <- function(obj, .df_row, arm, conf_level) {
224	!	alpha <- 1 - conf_level
225	!	p <- 1 - alpha / 2
226
227	!	arm_levels <- levels(.df_row[[arm]])
228
229	!	out <- lapply(arm_levels, function(lev) {
230	!	temp <- .df_row
231	!	temp[[arm]] <- factor(lev, levels = arm_levels)
232
233	!	mf <- stats::model.frame(obj$formula, data = temp)
234	!	X <- stats::model.matrix(obj$formula, data = mf) # nolint
235
236	!	rate <- stats::predict(obj, newdata = mf, type = "response")
237	!	rate_hat <- mean(rate)
238
239	!	zz <- colMeans(rate * X)
240	!	se <- sqrt(as.numeric(t(zz) %% stats::vcov(obj) %% zz))
241	!	rate_lwr <- rate_hat * exp(-stats::qnorm(p) * se / rate_hat)
242	!	rate_upr <- rate_hat * exp(stats::qnorm(p) * se / rate_hat)
243
244	!	c(rate_hat, rate_lwr, rate_upr)
245		})
246
247	!	names(out) <- arm_levels
248	!	out <- do.call(rbind, out)
249	!	if ("negbin" %in% class(obj)) {
250	!	colnames(out) <- c("response", "asymp.LCL", "asymp.UCL")
251		} else {
252	!	colnames(out) <- c("rate", "asymp.LCL", "asymp.UCL")
253		}
254	!	out <- as.data.frame(out)
255	!	out[[arm]] <- rownames(out)
256	!	out
257		}
258
259		#' @describeIn summarize_glm_count Statistics function that produces a named list of results
260		#' of the investigated Poisson model.
261		#'
262		#' @return
263		#' * `s_glm_count()` returns a named `list` of 5 statistics:
264		#' * `n`: Count of complete sample size for the group.
265		#' * `rate`: Estimated event rate per follow-up time.
266		#' * `rate_ci`: Confidence level for estimated rate per follow-up time.
267		#' * `rate_ratio`: Ratio of event rates in each treatment arm to the reference arm.
268		#' * `rate_ratio_ci`: Confidence level for the rate ratio.
269		#' * `pval`: p-value.
270		#'
271		#' @keywords internal
272		s_glm_count <- function(df,
273		.var,
274		.df_row,
275		variables,
276		.ref_group,
277		.in_ref_col,
278		distribution,
279		conf_level,
280		rate_mean_method,
281		weights,
282		scale = 1) {
283	5x	arm <- variables$arm
284
285	5x	y <- df[[.var]]
286	4x	smry_level <- as.character(unique(df[[arm]]))
287
288		# ensure there is only 1 value
289	4x	checkmate::assert_scalar(smry_level)
290
291	4x	results <- h_glm_count(
292	4x	.var = .var,
293	4x	.df_row = .df_row,
294	4x	variables = variables,
295	4x	distribution = distribution,
296	4x	weights
297		)
298
299	4x	if (rate_mean_method == "emmeans") {
300	4x	emmeans_smry <- summary(results$emmeans_fit, level = conf_level)
301	!	} else if (rate_mean_method == "ppmeans") {
302	!	emmeans_smry <- h_ppmeans(results$glm_fit, .df_row, arm, conf_level)
303		}
304
305	4x	emmeans_smry_level <- emmeans_smry[emmeans_smry[[arm]] == smry_level, ]
306
307	4x	if (.in_ref_col) {
308	2x	list(
309	2x	n = length(y[!is.na(y)]),
310	2x	rate = formatters::with_label(
311	2x	ifelse(distribution == "negbin", emmeans_smry_level$response * scale, emmeans_smry_level$rate),
312	2x	"Adjusted Rate"
313		),
314	2x	rate_ci = formatters::with_label(
315	2x	c(emmeans_smry_level$asymp.LCL * scale, emmeans_smry_level$asymp.UCL * scale),
316	2x	f_conf_level(conf_level)
317		),
318	2x	rate_ratio = formatters::with_label(character(), "Adjusted Rate Ratio"),
319	2x	rate_ratio_ci = formatters::with_label(character(), f_conf_level(conf_level)),
320	2x	pval = formatters::with_label(character(), "p-value")
321		)
322		} else {
323	2x	emmeans_contrasts <- emmeans::contrast(
324	2x	results$emmeans_fit,
325	2x	method = "trt.vs.ctrl",
326	2x	ref = grep(
327	2x	as.character(unique(.ref_group[[arm]])),
328	2x	as.data.frame(results$emmeans_fit)[[arm]]
329		)
330		)
331
332	2x	contrasts_smry <- summary(
333	2x	emmeans_contrasts,
334	2x	infer = TRUE,
335	2x	adjust = "none"
336		)
337
338	2x	smry_contrasts_level <- contrasts_smry[grepl(smry_level, contrasts_smry$contrast), ]
339
340	2x	list(
341	2x	n = length(y[!is.na(y)]),
342	2x	rate = formatters::with_label(
343	2x	ifelse(distribution == "negbin", emmeans_smry_level$response * scale, emmeans_smry_level$rate),
344	2x	"Adjusted Rate"
345		),
346	2x	rate_ci = formatters::with_label(
347	2x	c(emmeans_smry_level$asymp.LCL * scale, emmeans_smry_level$asymp.UCL * scale),
348	2x	f_conf_level(conf_level)
349		),
350	2x	rate_ratio = formatters::with_label(smry_contrasts_level$ratio, "Adjusted Rate Ratio"),
351	2x	rate_ratio_ci = formatters::with_label(
352	2x	c(smry_contrasts_level$asymp.LCL, smry_contrasts_level$asymp.UCL),
353	2x	f_conf_level(conf_level)
354		),
355	2x	pval = formatters::with_label(smry_contrasts_level$p.value, "p-value")
356		)
357		}
358		}
359
360		#' @describeIn summarize_glm_count Formatted analysis function which is used as `afun` in `summarize_glm_count()`.
361		#'
362		#' @return
363		#' * `a_glm_count()` returns the corresponding list with formatted [rtables::CellValue()].
364		#'
365		#' @keywords internal
366		a_glm_count <- make_afun(
367		s_glm_count,
368		.indent_mods = c(
369		"n" = 0L,
370		"rate" = 0L,
371		"rate_ci" = 1L,
372		"rate_ratio" = 0L,
373		"rate_ratio_ci" = 1L,
374		"pval" = 1L
375		),
376		.formats = c(
377		"n" = "xx",
378		"rate" = "xx.xxxx",
379		"rate_ci" = "(xx.xxxx, xx.xxxx)",
380		"rate_ratio" = "xx.xxxx",
381		"rate_ratio_ci" = "(xx.xxxx, xx.xxxx)",
382		"pval" = "x.xxxx \| (<0.0001)"
383		),
384		.null_ref_cells = FALSE
385		)
386
387		#' @describeIn summarize_glm_count Layout-creating function which can take statistics function arguments
388		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
389		#'
390		#' @return
391		#' * `summarize_glm_count()` returns a layout object suitable for passing to further layouting functions,
392		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
393		#' the statistics from `s_glm_count()` to the table layout.
394		#'
395		#' @examples
396		#' library(dplyr)
397		#'
398		#' anl <- tern_ex_adtte %>% filter(PARAMCD == "TNE")
399		#' anl$AVAL_f <- as.factor(anl$AVAL)
400		#'
401		#' lyt <- basic_table() %>%
402		#' split_cols_by("ARM", ref_group = "B: Placebo") %>%
403		#' add_colcounts() %>%
404		#' analyze_vars(
405		#' "AVAL_f",
406		#' var_labels = "Number of exacerbations per patient",
407		#' .stats = c("count_fraction"),
408		#' .formats = c("count_fraction" = "xx (xx.xx%)"),
409		#' .labels = c("Number of exacerbations per patient")
410		#' ) %>%
411		#' summarize_glm_count(
412		#' vars = "AVAL",
413		#' variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = NULL),
414		#' conf_level = 0.95,
415		#' distribution = "poisson",
416		#' rate_mean_method = "emmeans",
417		#' var_labels = "Unadjusted exacerbation rate (per year)",
418		#' table_names = "unadj",
419		#' .stats = c("rate"),
420		#' .labels = c(rate = "Rate")
421		#' ) %>%
422		#' summarize_glm_count(
423		#' vars = "AVAL",
424		#' variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
425		#' conf_level = 0.95,
426		#' distribution = "quasipoisson",
427		#' rate_mean_method = "ppmeans",
428		#' var_labels = "Adjusted (QP) exacerbation rate (per year)",
429		#' table_names = "adj",
430		#' .stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
431		#' .labels = c(
432		#' rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
433		#' rate_ratio_ci = "Rate Ratio CI", pval = "p value"
434		#' )
435		#' )
436		#'
437		#' build_table(lyt = lyt, df = anl)
438		#'
439		#' @export
440		#' @order 2
441		summarize_glm_count <- function(lyt,
442		vars,
443		variables,
444		distribution,
445		conf_level,
446		rate_mean_method,
447		weights = stats::weights,
448		scale = 1,
449		var_labels,
450		na_str = default_na_str(),
451		nested = TRUE,
452		...,
453		show_labels = "visible",
454		table_names = vars,
455		.stats = NULL,
456		.formats = NULL,
457		.labels = NULL,
458		.indent_mods = NULL) {
459	3x	extra_args <- list(
460	3x	variables = variables, distribution = distribution, conf_level = conf_level,
461	3x	rate_mean_method = rate_mean_method, weights = weights, scale = scale, ...
462		)
463
464	3x	afun <- make_afun(
465	3x	a_glm_count,
466	3x	.stats = .stats,
467	3x	.formats = .formats,
468	3x	.labels = .labels,
469	3x	.indent_mods = .indent_mods
470		)
471
472	3x	analyze(
473	3x	lyt,
474	3x	vars,
475	3x	var_labels = var_labels,
476	3x	show_labels = show_labels,
477	3x	table_names = table_names,
478	3x	afun = afun,
479	3x	na_str = na_str,
480	3x	nested = nested,
481	3x	extra_args = extra_args
482		)
483		}

1		#' Estimation of Proportions
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Estimate the proportion of responders within a studied population.
6		#'
7		#' @inheritParams prop_strat_wilson
8		#' @inheritParams argument_convention
9		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_proportion")`
10		#' to see available statistics for this function.
11		#' @param method (`string`)\cr the method used to construct the confidence interval
12		#' for proportion of successful outcomes; one of `waldcc`, `wald`, `clopper-pearson`,
13		#' `wilson`, `wilsonc`, `strat_wilson`, `strat_wilsonc`, `agresti-coull` or `jeffreys`.
14		#' @param long (`flag`)\cr a long description is required.
15		#'
16		#' @seealso [h_proportions]
17		#'
18		#' @name estimate_proportions
19		#' @order 1
20		NULL
21
22		#' @describeIn estimate_proportions Statistics function estimating a
23		#' proportion along with its confidence interval.
24		#'
25		#' @param df (`logical` or `data.frame`)\cr if only a logical vector is used,
26		#' it indicates whether each subject is a responder or not. `TRUE` represents
27		#' a successful outcome. If a `data.frame` is provided, also the `strata` variable
28		#' names must be provided in `variables` as a list element with the strata strings.
29		#' In the case of `data.frame`, the logical vector of responses must be indicated as a
30		#' variable name in `.var`.
31		#'
32		#' @return
33		#' * `s_proportion()` returns statistics `n_prop` (`n` and proportion) and `prop_ci` (proportion CI) for a
34		#' given variable.
35		#'
36		#' @examples
37		#' # Case with only logical vector.
38		#' rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0)
39		#' s_proportion(rsp_v)
40		#'
41		#' # Example for Stratified Wilson CI
42		#' nex <- 100 # Number of example rows
43		#' dta <- data.frame(
44		#' "rsp" = sample(c(TRUE, FALSE), nex, TRUE),
45		#' "grp" = sample(c("A", "B"), nex, TRUE),
46		#' "f1" = sample(c("a1", "a2"), nex, TRUE),
47		#' "f2" = sample(c("x", "y", "z"), nex, TRUE),
48		#' stringsAsFactors = TRUE
49		#' )
50		#'
51		#' s_proportion(
52		#' df = dta,
53		#' .var = "rsp",
54		#' variables = list(strata = c("f1", "f2")),
55		#' conf_level = 0.90,
56		#' method = "strat_wilson"
57		#' )
58		#'
59		#' @export
60		s_proportion <- function(df,
61		.var,
62		conf_level = 0.95,
63		method = c(
64		"waldcc", "wald", "clopper-pearson",
65		"wilson", "wilsonc", "strat_wilson", "strat_wilsonc",
66		"agresti-coull", "jeffreys"
67		),
68		weights = NULL,
69		max_iterations = 50,
70		variables = list(strata = NULL),
71		long = FALSE) {
72	135x	method <- match.arg(method)
73	135x	checkmate::assert_flag(long)
74	135x	assert_proportion_value(conf_level)
75
76	135x	if (!is.null(variables$strata)) {
77		# Checks for strata
78	!	if (missing(df)) stop("When doing stratified analysis a data.frame with specific columns is needed.")
79	!	strata_colnames <- variables$strata
80	!	checkmate::assert_character(strata_colnames, null.ok = FALSE)
81	!	strata_vars <- stats::setNames(as.list(strata_colnames), strata_colnames)
82	!	assert_df_with_variables(df, strata_vars)
83
84	!	strata <- interaction(df[strata_colnames])
85	!	strata <- as.factor(strata)
86
87		# Pushing down checks to prop_strat_wilson
88	135x	} else if (checkmate::test_subset(method, c("strat_wilson", "strat_wilsonc"))) {
89	!	stop("To use stratified methods you need to specify the strata variables.")
90		}
91	135x	if (checkmate::test_atomic_vector(df)) {
92	135x	rsp <- as.logical(df)
93		} else {
94	!	rsp <- as.logical(df[[.var]])
95		}
96	135x	n <- sum(rsp)
97	135x	p_hat <- mean(rsp)
98
99	135x	prop_ci <- switch(method,
100	135x	"clopper-pearson" = prop_clopper_pearson(rsp, conf_level),
101	135x	"wilson" = prop_wilson(rsp, conf_level),
102	135x	"wilsonc" = prop_wilson(rsp, conf_level, correct = TRUE),
103	135x	"strat_wilson" = prop_strat_wilson(rsp,
104	135x	strata,
105	135x	weights,
106	135x	conf_level,
107	135x	max_iterations,
108	135x	correct = FALSE
109	135x	)$conf_int,
110	135x	"strat_wilsonc" = prop_strat_wilson(rsp,
111	135x	strata,
112	135x	weights,
113	135x	conf_level,
114	135x	max_iterations,
115	135x	correct = TRUE
116	135x	)$conf_int,
117	135x	"wald" = prop_wald(rsp, conf_level),
118	135x	"waldcc" = prop_wald(rsp, conf_level, correct = TRUE),
119	135x	"agresti-coull" = prop_agresti_coull(rsp, conf_level),
120	135x	"jeffreys" = prop_jeffreys(rsp, conf_level)
121		)
122
123	135x	list(
124	135x	"n_prop" = formatters::with_label(c(n, p_hat), "Responders"),
125	135x	"prop_ci" = formatters::with_label(
126	135x	x = 100 * prop_ci, label = d_proportion(conf_level, method, long = long)
127		)
128		)
129		}
130
131		#' @describeIn estimate_proportions Formatted analysis function which is used as `afun`
132		#' in `estimate_proportion()`.
133		#'
134		#' @return
135		#' * `a_proportion()` returns the corresponding list with formatted [rtables::CellValue()].
136		#'
137		#' @export
138		a_proportion <- make_afun(
139		s_proportion,
140		.formats = c(n_prop = "xx (xx.x%)", prop_ci = "(xx.x, xx.x)")
141		)
142
143		#' @describeIn estimate_proportions Layout-creating function which can take statistics function arguments
144		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
145		#'
146		#' @return
147		#' * `estimate_proportion()` returns a layout object suitable for passing to further layouting functions,
148		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
149		#' the statistics from `s_proportion()` to the table layout.
150		#'
151		#' @examples
152		#' dta_test <- data.frame(
153		#' USUBJID = paste0("S", 1:12),
154		#' ARM = rep(LETTERS[1:3], each = 4),
155		#' AVAL = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0))
156		#' )
157		#'
158		#' basic_table() %>%
159		#' split_cols_by("ARM") %>%
160		#' estimate_proportion(vars = "AVAL") %>%
161		#' build_table(df = dta_test)
162		#'
163		#' @export
164		#' @order 2
165		estimate_proportion <- function(lyt,
166		vars,
167		conf_level = 0.95,
168		method = c(
169		"waldcc", "wald", "clopper-pearson",
170		"wilson", "wilsonc", "strat_wilson", "strat_wilsonc",
171		"agresti-coull", "jeffreys"
172		),
173		weights = NULL,
174		max_iterations = 50,
175		variables = list(strata = NULL),
176		long = FALSE,
177		na_str = default_na_str(),
178		nested = TRUE,
179		...,
180		show_labels = "hidden",
181		table_names = vars,
182		.stats = NULL,
183		.formats = NULL,
184		.labels = NULL,
185		.indent_mods = NULL) {
186	3x	extra_args <- list(
187	3x	conf_level = conf_level, method = method, weights = weights, max_iterations = max_iterations,
188	3x	variables = variables, long = long, ...
189		)
190
191	3x	afun <- make_afun(
192	3x	a_proportion,
193	3x	.stats = .stats,
194	3x	.formats = .formats,
195	3x	.labels = .labels,
196	3x	.indent_mods = .indent_mods
197		)
198	3x	analyze(
199	3x	lyt,
200	3x	vars,
201	3x	afun = afun,
202	3x	na_str = na_str,
203	3x	nested = nested,
204	3x	extra_args = extra_args,
205	3x	show_labels = show_labels,
206	3x	table_names = table_names
207		)
208		}
209
210		#' Helper Functions for Calculating Proportion Confidence Intervals
211		#'
212		#' @description `r lifecycle::badge("stable")`
213		#'
214		#' Functions to calculate different proportion confidence intervals for use in [estimate_proportion()].
215		#'
216		#' @inheritParams argument_convention
217		#' @inheritParams estimate_proportions
218		#'
219		#' @return Confidence interval of a proportion.
220		#'
221		#' @seealso [estimate_proportions], descriptive function [d_proportion()],
222		#' and helper functions [strata_normal_quantile()] and [update_weights_strat_wilson()].
223		#'
224		#' @name h_proportions
225		NULL
226
227		#' @describeIn h_proportions Calculates the Wilson interval by calling [stats::prop.test()].
228		#' Also referred to as Wilson score interval.
229		#'
230		#' @examples
231		#' rsp <- c(
232		#' TRUE, TRUE, TRUE, TRUE, TRUE,
233		#' FALSE, FALSE, FALSE, FALSE, FALSE
234		#' )
235		#' prop_wilson(rsp, conf_level = 0.9)
236		#'
237		#' @export
238		prop_wilson <- function(rsp, conf_level, correct = FALSE) {
239	5x	y <- stats::prop.test(
240	5x	sum(rsp),
241	5x	length(rsp),
242	5x	correct = correct,
243	5x	conf.level = conf_level
244		)
245
246	5x	as.numeric(y$conf.int)
247		}
248
249		#' @describeIn h_proportions Calculates the stratified Wilson confidence
250		#' interval for unequal proportions as described in \insertCite{Yan2010-jt;textual}{tern}
251		#'
252		#' @param strata (`factor`)\cr variable with one level per stratum and same length as `rsp`.
253		#' @param weights (`numeric` or `NULL`)\cr weights for each level of the strata. If `NULL`, they are
254		#' estimated using the iterative algorithm proposed in \insertCite{Yan2010-jt;textual}{tern} that
255		#' minimizes the weighted squared length of the confidence interval.
256		#' @param max_iterations (`count`)\cr maximum number of iterations for the iterative procedure used
257		#' to find estimates of optimal weights.
258		#' @param correct (`flag`)\cr include the continuity correction. For further information, see for example
259		#' [stats::prop.test()].
260		#'
261		#' @references
262		#' \insertRef{Yan2010-jt}{tern}
263		#'
264		#' @examples
265		#' # Stratified Wilson confidence interval with unequal probabilities
266		#'
267		#' set.seed(1)
268		#' rsp <- sample(c(TRUE, FALSE), 100, TRUE)
269		#' strata_data <- data.frame(
270		#' "f1" = sample(c("a", "b"), 100, TRUE),
271		#' "f2" = sample(c("x", "y", "z"), 100, TRUE),
272		#' stringsAsFactors = TRUE
273		#' )
274		#' strata <- interaction(strata_data)
275		#' n_strata <- ncol(table(rsp, strata)) # Number of strata
276		#'
277		#' prop_strat_wilson(
278		#' rsp = rsp, strata = strata,
279		#' conf_level = 0.90
280		#' )
281		#'
282		#' # Not automatic setting of weights
283		#' prop_strat_wilson(
284		#' rsp = rsp, strata = strata,
285		#' weights = rep(1 / n_strata, n_strata),
286		#' conf_level = 0.90
287		#' )
288		#'
289		#' @export
290		prop_strat_wilson <- function(rsp,
291		strata,
292		weights = NULL,
293		conf_level = 0.95,
294		max_iterations = NULL,
295		correct = FALSE) {
296	20x	checkmate::assert_logical(rsp, any.missing = FALSE)
297	20x	checkmate::assert_factor(strata, len = length(rsp))
298	20x	assert_proportion_value(conf_level)
299
300	20x	tbl <- table(rsp, strata)
301	20x	n_strata <- length(unique(strata))
302
303		# Checking the weights and maximum number of iterations.
304	20x	do_iter <- FALSE
305	20x	if (is.null(weights)) {
306	6x	weights <- rep(1 / n_strata, n_strata) # Initialization for iterative procedure
307	6x	do_iter <- TRUE
308
309		# Iteration parameters
310	2x	if (is.null(max_iterations)) max_iterations <- 10
311	6x	checkmate::assert_int(max_iterations, na.ok = FALSE, null.ok = FALSE, lower = 1)
312		}
313	20x	checkmate::assert_numeric(weights, lower = 0, upper = 1, any.missing = FALSE, len = n_strata)
314	20x	sum_weights <- checkmate::assert_int(sum(weights))
315	!	if (as.integer(sum_weights + 0.5) != 1L) stop("Sum of weights must be 1L.")
316
317
318	20x	xs <- tbl["TRUE", ]
319	20x	ns <- colSums(tbl)
320	20x	use_stratum <- (ns > 0)
321	20x	ns <- ns[use_stratum]
322	20x	xs <- xs[use_stratum]
323	20x	ests <- xs / ns
324	20x	vars <- ests * (1 - ests) / ns
325
326	20x	strata_qnorm <- strata_normal_quantile(vars, weights, conf_level)
327
328		# Iterative setting of weights if they were not set externally
329	20x	weights_new <- if (do_iter) {
330	6x	update_weights_strat_wilson(vars, strata_qnorm, weights, ns, max_iterations, conf_level)$weights
331		} else {
332	14x	weights
333		}
334
335	20x	strata_conf_level <- 2 * stats::pnorm(strata_qnorm) - 1
336
337	20x	ci_by_strata <- Map(
338	20x	function(x, n) {
339		# Classic Wilson's confidence interval
340	139x	suppressWarnings(stats::prop.test(x, n, correct = correct, conf.level = strata_conf_level)$conf.int)
341		},
342	20x	x = xs,
343	20x	n = ns
344		)
345	20x	lower_by_strata <- sapply(ci_by_strata, "[", 1L)
346	20x	upper_by_strata <- sapply(ci_by_strata, "[", 2L)
347
348	20x	lower <- sum(weights_new * lower_by_strata)
349	20x	upper <- sum(weights_new * upper_by_strata)
350
351		# Return values
352	20x	if (do_iter) {
353	6x	list(
354	6x	conf_int = c(
355	6x	lower = lower,
356	6x	upper = upper
357		),
358	6x	weights = weights_new
359		)
360		} else {
361	14x	list(
362	14x	conf_int = c(
363	14x	lower = lower,
364	14x	upper = upper
365		)
366		)
367		}
368		}
369
370		#' @describeIn h_proportions Calculates the Clopper-Pearson interval by calling [stats::binom.test()].
371		#' Also referred to as the `exact` method.
372		#'
373		#' @examples
374		#' prop_clopper_pearson(rsp, conf_level = .95)
375		#'
376		#' @export
377		prop_clopper_pearson <- function(rsp,
378		conf_level) {
379	1x	y <- stats::binom.test(
380	1x	x = sum(rsp),
381	1x	n = length(rsp),
382	1x	conf.level = conf_level
383		)
384	1x	as.numeric(y$conf.int)
385		}
386
387		#' @describeIn h_proportions Calculates the Wald interval by following the usual textbook definition
388		#' for a single proportion confidence interval using the normal approximation.
389		#'
390		#' @param correct (`flag`)\cr apply continuity correction.
391		#'
392		#' @examples
393		#' prop_wald(rsp, conf_level = 0.95)
394		#' prop_wald(rsp, conf_level = 0.95, correct = TRUE)
395		#'
396		#' @export
397		prop_wald <- function(rsp, conf_level, correct = FALSE) {
398	132x	n <- length(rsp)
399	132x	p_hat <- mean(rsp)
400	132x	z <- stats::qnorm((1 + conf_level) / 2)
401	132x	q_hat <- 1 - p_hat
402	132x	correct <- if (correct) 1 / (2 * n) else 0
403
404	132x	err <- z * sqrt(p_hat * q_hat) / sqrt(n) + correct
405	132x	l_ci <- max(0, p_hat - err)
406	132x	u_ci <- min(1, p_hat + err)
407
408	132x	c(l_ci, u_ci)
409		}
410
411		#' @describeIn h_proportions Calculates the `Agresti-Coull` interval (created by `Alan Agresti` and `Brent Coull`) by
412		#' (for 95% CI) adding two successes and two failures to the data and then using the Wald formula to construct a CI.
413		#'
414		#' @examples
415		#' prop_agresti_coull(rsp, conf_level = 0.95)
416		#'
417		#' @export
418		prop_agresti_coull <- function(rsp, conf_level) {
419	2x	n <- length(rsp)
420	2x	x_sum <- sum(rsp)
421	2x	z <- stats::qnorm((1 + conf_level) / 2)
422
423		# Add here both z^2 / 2 successes and failures.
424	2x	x_sum_tilde <- x_sum + z^2 / 2
425	2x	n_tilde <- n + z^2
426
427		# Then proceed as with the Wald interval.
428	2x	p_tilde <- x_sum_tilde / n_tilde
429	2x	q_tilde <- 1 - p_tilde
430	2x	err <- z * sqrt(p_tilde * q_tilde) / sqrt(n_tilde)
431	2x	l_ci <- max(0, p_tilde - err)
432	2x	u_ci <- min(1, p_tilde + err)
433
434	2x	c(l_ci, u_ci)
435		}
436
437		#' @describeIn h_proportions Calculates the Jeffreys interval, an equal-tailed interval based on the
438		#' non-informative Jeffreys prior for a binomial proportion.
439		#'
440		#' @examples
441		#' prop_jeffreys(rsp, conf_level = 0.95)
442		#'
443		#' @export
444		prop_jeffreys <- function(rsp,
445		conf_level) {
446	4x	n <- length(rsp)
447	4x	x_sum <- sum(rsp)
448
449	4x	alpha <- 1 - conf_level
450	4x	l_ci <- ifelse(
451	4x	x_sum == 0,
452	4x	0,
453	4x	stats::qbeta(alpha / 2, x_sum + 0.5, n - x_sum + 0.5)
454		)
455
456	4x	u_ci <- ifelse(
457	4x	x_sum == n,
458	4x	1,
459	4x	stats::qbeta(1 - alpha / 2, x_sum + 0.5, n - x_sum + 0.5)
460		)
461
462	4x	c(l_ci, u_ci)
463		}
464
465		#' Description of the Proportion Summary
466		#'
467		#' @description `r lifecycle::badge("stable")`
468		#'
469		#' This is a helper function that describes the analysis in [s_proportion()].
470		#'
471		#' @inheritParams s_proportion
472		#' @param long (`flag`)\cr whether a long or a short (default) description is required.
473		#'
474		#' @return String describing the analysis.
475		#'
476		#' @export
477		d_proportion <- function(conf_level,
478		method,
479		long = FALSE) {
480	147x	label <- paste0(conf_level * 100, "% CI")
481
482	!	if (long) label <- paste(label, "for Response Rates")
483
484	147x	method_part <- switch(method,
485	147x	"clopper-pearson" = "Clopper-Pearson",
486	147x	"waldcc" = "Wald, with correction",
487	147x	"wald" = "Wald, without correction",
488	147x	"wilson" = "Wilson, without correction",
489	147x	"strat_wilson" = "Stratified Wilson, without correction",
490	147x	"wilsonc" = "Wilson, with correction",
491	147x	"strat_wilsonc" = "Stratified Wilson, with correction",
492	147x	"agresti-coull" = "Agresti-Coull",
493	147x	"jeffreys" = "Jeffreys",
494	147x	stop(paste(method, "does not have a description"))
495		)
496
497	147x	paste0(label, " (", method_part, ")")
498		}
499
500		#' Helper Function for the Estimation of Stratified Quantiles
501		#'
502		#' @description `r lifecycle::badge("stable")`
503		#'
504		#' This function wraps the estimation of stratified percentiles when we assume
505		#' the approximation for large numbers. This is necessary only in the case
506		#' proportions for each strata are unequal.
507		#'
508		#' @inheritParams argument_convention
509		#' @inheritParams prop_strat_wilson
510		#'
511		#' @return Stratified quantile.
512		#'
513		#' @seealso [prop_strat_wilson()]
514		#'
515		#' @examples
516		#' strata_data <- table(data.frame(
517		#' "f1" = sample(c(TRUE, FALSE), 100, TRUE),
518		#' "f2" = sample(c("x", "y", "z"), 100, TRUE),
519		#' stringsAsFactors = TRUE
520		#' ))
521		#' ns <- colSums(strata_data)
522		#' ests <- strata_data["TRUE", ] / ns
523		#' vars <- ests * (1 - ests) / ns
524		#' weights <- rep(1 / length(ns), length(ns))
525		#'
526		#' strata_normal_quantile(vars, weights, 0.95)
527		#'
528		#' @export
529		strata_normal_quantile <- function(vars, weights, conf_level) {
530	41x	summands <- weights^2 * vars
531		# Stratified quantile
532	41x	sqrt(sum(summands)) / sum(sqrt(summands)) * stats::qnorm((1 + conf_level) / 2)
533		}
534
535		#' Helper Function for the Estimation of Weights for `prop_strat_wilson`
536		#'
537		#' @description `r lifecycle::badge("stable")`
538		#'
539		#' This function wraps the iteration procedure that allows you to estimate
540		#' the weights for each proportional strata. This assumes to minimize the
541		#' weighted squared length of the confidence interval.
542		#'
543		#' @inheritParams prop_strat_wilson
544		#' @param vars (`numeric`)\cr normalized proportions for each strata.
545		#' @param strata_qnorm (`numeric`)\cr initial estimation with identical weights of the quantiles.
546		#' @param initial_weights (`numeric`)\cr initial weights used to calculate `strata_qnorm`. This can
547		#' be optimized in the future if we need to estimate better initial weights.
548		#' @param n_per_strata (`numeric`)\cr number of elements in each strata.
549		#' @param max_iterations (`count`)\cr maximum number of iterations to be tried. Convergence is always checked.
550		#' @param tol (`number`)\cr tolerance threshold for convergence.
551		#'
552		#' @return A `list` of 3 elements: `n_it`, `weights`, and `diff_v`.
553		#'
554		#' @seealso For references and details see [prop_strat_wilson()].
555		#'
556		#' @examples
557		#' vs <- c(0.011, 0.013, 0.012, 0.014, 0.017, 0.018)
558		#' sq <- 0.674
559		#' ws <- rep(1 / length(vs), length(vs))
560		#' ns <- c(22, 18, 17, 17, 14, 12)
561		#'
562		#' update_weights_strat_wilson(vs, sq, ws, ns, 100, 0.95, 0.001)
563		#'
564		#' @export
565		update_weights_strat_wilson <- function(vars,
566		strata_qnorm,
567		initial_weights,
568		n_per_strata,
569		max_iterations = 50,
570		conf_level = 0.95,
571		tol = 0.001) {
572	8x	it <- 0
573	8x	diff_v <- NULL
574
575	8x	while (it < max_iterations) {
576	19x	it <- it + 1
577	19x	weights_new_t <- (1 + strata_qnorm^2 / n_per_strata)^2
578	19x	weights_new_b <- (vars + strata_qnorm^2 / (4 * n_per_strata^2))
579	19x	weights_new <- weights_new_t / weights_new_b
580	19x	weights_new <- weights_new / sum(weights_new)
581	19x	strata_qnorm <- strata_normal_quantile(vars, weights_new, conf_level)
582	19x	diff_v <- c(diff_v, sum(abs(weights_new - initial_weights)))
583	8x	if (diff_v[length(diff_v)] < tol) break
584	11x	initial_weights <- weights_new
585		}
586
587	8x	if (it == max_iterations) {
588	!	warning("The heuristic to find weights did not converge with max_iterations = ", max_iterations)
589		}
590
591	8x	list(
592	8x	"n_it" = it,
593	8x	"weights" = weights_new,
594	8x	"diff_v" = diff_v
595		)
596		}

1		#' Count Patients with Marked Laboratory Abnormalities
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Primary analysis variable `.var` indicates whether single, replicated or last marked laboratory
6		#' abnormality was observed (`factor`). Additional analysis variables are `id` (`character` or `factor`)
7		#' and `direction` (`factor`) indicating the direction of the abnormality. Denominator is number of
8		#' patients with at least one valid measurement during the analysis.
9		#' * For `Single, not last` and `Last or replicated`: Numerator is number of patients
10		#' with `Single, not last` and `Last or replicated` levels, respectively.
11		#' * For `Any`: Numerator is the number of patients with either single or
12		#' replicated marked abnormalities.
13		#'
14		#' @inheritParams argument_convention
15		#' @param category (`list`)\cr with different marked category names for single
16		#' and last or replicated.
17		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("abnormal_by_marked")`
18		#' to see available statistics for this function.
19		#'
20		#' @note `Single, not last` and `Last or replicated` levels are mutually exclusive. If a patient has
21		#' abnormalities that meet both the `Single, not last` and `Last or replicated` criteria, then the
22		#' patient will be counted only under the `Last or replicated` category.
23		#'
24		#' @name abnormal_by_marked
25		#' @order 1
26		NULL
27
28		#' @describeIn abnormal_by_marked Statistics function for patients with marked lab abnormalities.
29		#'
30		#' @return
31		#' * `s_count_abnormal_by_marked()` returns statistic `count_fraction` with `Single, not last`,
32		#' `Last or replicated`, and `Any` results.
33		#'
34		#' @keywords internal
35		s_count_abnormal_by_marked <- function(df,
36		.var = "AVALCAT1",
37		.spl_context,
38		category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
39		variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")) {
40	3x	checkmate::assert_string(.var)
41	3x	checkmate::assert_list(variables)
42	3x	checkmate::assert_list(category)
43	3x	checkmate::assert_subset(names(category), c("single", "last_replicated"))
44	3x	checkmate::assert_subset(names(variables), c("id", "param", "direction"))
45	3x	checkmate::assert_vector(unique(df[[variables$direction]]), max.len = 1)
46
47	2x	assert_df_with_variables(df, c(aval = .var, variables))
48	2x	checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))
49	2x	checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))
50
51
52	2x	first_row <- .spl_context[.spl_context$split == variables[["param"]], ]
53		# Patients in the denominator have at least one post-baseline visit.
54	2x	subj <- first_row$full_parent_df[[1]][[variables[["id"]]]]
55	2x	subj_cur_col <- subj[first_row$cur_col_subset[[1]]]
56		# Some subjects may have a record for high and low directions but
57		# should be counted only once.
58	2x	denom <- length(unique(subj_cur_col))
59
60	2x	if (denom != 0) {
61	2x	subjects_last_replicated <- unique(
62	2x	df[df[[.var]] %in% category[["last_replicated"]], variables$id, drop = TRUE]
63		)
64	2x	subjects_single <- unique(
65	2x	df[df[[.var]] %in% category[["single"]], variables$id, drop = TRUE]
66		)
67		# Subjects who have both single and last/replicated abnormalities are counted in only the last/replicated group.
68	2x	subjects_single <- setdiff(subjects_single, subjects_last_replicated)
69	2x	n_single <- length(subjects_single)
70	2x	n_last_replicated <- length(subjects_last_replicated)
71	2x	n_any <- n_single + n_last_replicated
72	2x	result <- list(count_fraction = list(
73	2x	"Single, not last" = c(n_single, n_single / denom),
74	2x	"Last or replicated" = c(n_last_replicated, n_last_replicated / denom),
75	2x	"Any Abnormality" = c(n_any, n_any / denom)
76		))
77		} else {
78	!	result <- list(count_fraction = list(
79	!	"Single, not last" = c(0, 0),
80	!	"Last or replicated" = c(0, 0),
81	!	"Any Abnormality" = c(0, 0)
82		))
83		}
84
85	2x	result
86		}
87
88		#' @describeIn abnormal_by_marked Formatted analysis function which is used as `afun`
89		#' in `count_abnormal_by_marked()`.
90		#'
91		#' @return
92		#' * `a_count_abnormal_by_marked()` returns the corresponding list with formatted [rtables::CellValue()].
93		#'
94		#' @keywords internal
95		a_count_abnormal_by_marked <- make_afun(
96		s_count_abnormal_by_marked,
97		.formats = c(count_fraction = format_count_fraction)
98		)
99
100		#' @describeIn abnormal_by_marked Layout-creating function which can take statistics function arguments
101		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
102		#'
103		#' @return
104		#' * `count_abnormal_by_marked()` returns a layout object suitable for passing to further layouting functions,
105		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
106		#' the statistics from `s_count_abnormal_by_marked()` to the table layout.
107		#'
108		#' @examples
109		#' library(dplyr)
110		#'
111		#' df <- data.frame(
112		#' USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))),
113		#' ARMCD = factor(c(rep("ARM A", 5), rep("ARM B", 5), rep("ARM A", 5), rep("ARM B", 5))),
114		#' ANRIND = factor(c(
115		#' "NORMAL", "HIGH", "HIGH", "HIGH HIGH", "HIGH",
116		#' "HIGH", "HIGH", "HIGH HIGH", "NORMAL", "HIGH HIGH", "NORMAL", "LOW", "LOW", "LOW LOW", "LOW",
117		#' "LOW", "LOW", "LOW LOW", "NORMAL", "LOW LOW"
118		#' )),
119		#' ONTRTFL = rep(c("", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"), 2),
120		#' PARAMCD = factor(c(rep("CRP", 10), rep("ALT", 10))),
121		#' AVALCAT1 = factor(rep(c("", "", "", "SINGLE", "REPLICATED", "", "", "LAST", "", "SINGLE"), 2)),
122		#' stringsAsFactors = FALSE
123		#' )
124		#'
125		#' df <- df %>%
126		#' mutate(abn_dir = factor(
127		#' case_when(
128		#' ANRIND == "LOW LOW" ~ "Low",
129		#' ANRIND == "HIGH HIGH" ~ "High",
130		#' TRUE ~ ""
131		#' ),
132		#' levels = c("Low", "High")
133		#' ))
134		#'
135		#' # Select only post-baseline records.
136		#' df <- df %>% filter(ONTRTFL == "Y")
137		#' df_crp <- df %>%
138		#' filter(PARAMCD == "CRP") %>%
139		#' droplevels()
140		#' full_parent_df <- list(df_crp, "not_needed")
141		#' cur_col_subset <- list(rep(TRUE, nrow(df_crp)), "not_needed")
142		#' spl_context <- data.frame(
143		#' split = c("PARAMCD", "GRADE_DIR"),
144		#' full_parent_df = I(full_parent_df),
145		#' cur_col_subset = I(cur_col_subset)
146		#' )
147		#'
148		#' map <- unique(
149		#' df[df$abn_dir %in% c("Low", "High") & df$AVALCAT1 != "", c("PARAMCD", "abn_dir")]
150		#' ) %>%
151		#' lapply(as.character) %>%
152		#' as.data.frame() %>%
153		#' arrange(PARAMCD, abn_dir)
154		#'
155		#' basic_table() %>%
156		#' split_cols_by("ARMCD") %>%
157		#' split_rows_by("PARAMCD") %>%
158		#' summarize_num_patients(
159		#' var = "USUBJID",
160		#' .stats = "unique_count"
161		#' ) %>%
162		#' split_rows_by(
163		#' "abn_dir",
164		#' split_fun = trim_levels_to_map(map)
165		#' ) %>%
166		#' count_abnormal_by_marked(
167		#' var = "AVALCAT1",
168		#' variables = list(
169		#' id = "USUBJID",
170		#' param = "PARAMCD",
171		#' direction = "abn_dir"
172		#' )
173		#' ) %>%
174		#' build_table(df = df)
175		#'
176		#' basic_table() %>%
177		#' split_cols_by("ARMCD") %>%
178		#' split_rows_by("PARAMCD") %>%
179		#' summarize_num_patients(
180		#' var = "USUBJID",
181		#' .stats = "unique_count"
182		#' ) %>%
183		#' split_rows_by(
184		#' "abn_dir",
185		#' split_fun = trim_levels_in_group("abn_dir")
186		#' ) %>%
187		#' count_abnormal_by_marked(
188		#' var = "AVALCAT1",
189		#' variables = list(
190		#' id = "USUBJID",
191		#' param = "PARAMCD",
192		#' direction = "abn_dir"
193		#' )
194		#' ) %>%
195		#' build_table(df = df)
196		#'
197		#' @export
198		#' @order 2
199		count_abnormal_by_marked <- function(lyt,
200		var,
201		category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
202		variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir"),
203		na_str = default_na_str(),
204		nested = TRUE,
205		...,
206		.stats = NULL,
207		.formats = NULL,
208		.labels = NULL,
209		.indent_mods = NULL) {
210	1x	checkmate::assert_string(var)
211
212	1x	extra_args <- list(category = category, variables = variables, ...)
213
214	1x	afun <- make_afun(
215	1x	a_count_abnormal_by_marked,
216	1x	.stats = .stats,
217	1x	.formats = .formats,
218	1x	.labels = .labels,
219	1x	.indent_mods = .indent_mods,
220	1x	.ungroup_stats = "count_fraction"
221		)
222
223	1x	lyt <- analyze(
224	1x	lyt = lyt,
225	1x	vars = var,
226	1x	afun = afun,
227	1x	na_str = na_str,
228	1x	nested = nested,
229	1x	show_labels = "hidden",
230	1x	extra_args = extra_args
231		)
232	1x	lyt
233		}

1		#' Controls for Cox Regression
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Sets a list of parameters for Cox regression fit. Used internally.
6		#'
7		#' @inheritParams argument_convention
8		#' @param pval_method (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
9		#' @param interaction (`flag`)\cr if `TRUE`, the model includes the interaction between the studied
10		#' treatment and candidate covariate. Note that for univariate models without treatment arm, and
11		#' multivariate models, no interaction can be used so that this needs to be `FALSE`.
12		#' @param ties (`string`)\cr among `exact` (equivalent to `DISCRETE` in SAS), `efron` and `breslow`,
13		#' see [survival::coxph()]. Note: there is no equivalent of SAS `EXACT` method in R.
14		#'
15		#' @return A `list` of items with names corresponding to the arguments.
16		#'
17		#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()].
18		#'
19		#' @examples
20		#' control_coxreg()
21		#'
22		#' @export
23		control_coxreg <- function(pval_method = c("wald", "likelihood"),
24		ties = c("exact", "efron", "breslow"),
25		conf_level = 0.95,
26		interaction = FALSE) {
27	48x	pval_method <- match.arg(pval_method)
28	48x	ties <- match.arg(ties)
29	48x	checkmate::assert_flag(interaction)
30	48x	assert_proportion_value(conf_level)
31	48x	list(
32	48x	pval_method = pval_method,
33	48x	ties = ties,
34	48x	conf_level = conf_level,
35	48x	interaction = interaction
36		)
37		}
38
39		#' Custom Tidy Methods for Cox Regression
40		#'
41		#' @description `r lifecycle::badge("stable")`
42		#'
43		#' @inheritParams argument_convention
44		#' @param x (`list`)\cr Result of the Cox regression model fitted by [fit_coxreg_univar()] (for univariate models)
45		#' or [fit_coxreg_multivar()] (for multivariate models).
46		#'
47		#' @return [tidy()] returns:
48		#' * For `summary.coxph` objects, a `data.frame` with columns: `Pr(>\|z\|)`, `exp(coef)`, `exp(-coef)`, `lower .95`,
49		#' `upper .95`, `level`, and `n`.
50		#' * For `coxreg.univar` objects, a `data.frame` with columns: `effect`, `term`, `term_label`, `level`, `n`, `hr`,
51		#' `lcl`, `ucl`, `pval`, and `ci`.
52		#' * For `coxreg.multivar` objects, a `data.frame` with columns: `term`, `pval`, `term_label`, `hr`, `lcl`, `ucl`,
53		#' `level`, and `ci`.
54		#'
55		#' @seealso [cox_regression]
56		#'
57		#' @name tidy_coxreg
58		NULL
59
60		#' @describeIn tidy_coxreg Custom tidy method for [survival::coxph()] summary results.
61		#'
62		#' Tidy the [survival::coxph()] results into a `data.frame` to extract model results.
63		#'
64		#' @method tidy summary.coxph
65		#'
66		#' @examples
67		#' library(survival)
68		#' library(broom)
69		#'
70		#' set.seed(1, kind = "Mersenne-Twister")
71		#'
72		#' dta_bladder <- with(
73		#' data = bladder[bladder$enum < 5, ],
74		#' data.frame(
75		#' time = stop,
76		#' status = event,
77		#' armcd = as.factor(rx),
78		#' covar1 = as.factor(enum),
79		#' covar2 = factor(
80		#' sample(as.factor(enum)),
81		#' levels = 1:4, labels = c("F", "F", "M", "M")
82		#' )
83		#' )
84		#' )
85		#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
86		#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
87		#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
88		#'
89		#' formula <- "survival::Surv(time, status) ~ armcd + covar1"
90		#' msum <- summary(coxph(stats::as.formula(formula), data = dta_bladder))
91		#' tidy(msum)
92		#'
93		#' @export
94		tidy.summary.coxph <- function(x, # nolint
95		...) {
96	136x	checkmate::assert_class(x, "summary.coxph")
97	136x	pval <- x$coefficients
98	136x	confint <- x$conf.int
99	136x	levels <- rownames(pval)
100
101	136x	pval <- tibble::as_tibble(pval)
102	136x	confint <- tibble::as_tibble(confint)
103
104	136x	ret <- cbind(pval[, grepl("Pr", names(pval))], confint)
105	136x	ret$level <- levels
106	136x	ret$n <- x[["n"]]
107	136x	ret
108		}
109
110		#' @describeIn tidy_coxreg Custom tidy method for a univariate Cox regression.
111		#'
112		#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_univar()].
113		#'
114		#' @method tidy coxreg.univar
115		#'
116		#' @examples
117		#' ## Cox regression: arm + 1 covariate.
118		#' mod1 <- fit_coxreg_univar(
119		#' variables = list(
120		#' time = "time", event = "status", arm = "armcd",
121		#' covariates = "covar1"
122		#' ),
123		#' data = dta_bladder,
124		#' control = control_coxreg(conf_level = 0.91)
125		#' )
126		#'
127		#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
128		#' mod2 <- fit_coxreg_univar(
129		#' variables = list(
130		#' time = "time", event = "status", arm = "armcd",
131		#' covariates = c("covar1", "covar2")
132		#' ),
133		#' data = dta_bladder,
134		#' control = control_coxreg(conf_level = 0.91, interaction = TRUE)
135		#' )
136		#'
137		#' tidy(mod1)
138		#' tidy(mod2)
139		#'
140		#' @export
141		tidy.coxreg.univar <- function(x, # nolint
142		...) {
143	29x	checkmate::assert_class(x, "coxreg.univar")
144	29x	mod <- x$mod
145	29x	vars <- c(x$vars$arm, x$vars$covariates)
146	29x	has_arm <- "arm" %in% names(x$vars)
147
148	29x	result <- if (!has_arm) {
149	5x	Map(
150	5x	mod = mod, vars = vars,
151	5x	f = function(mod, vars) {
152	6x	h_coxreg_multivar_extract(
153	6x	var = vars,
154	6x	data = x$data,
155	6x	mod = mod,
156	6x	control = x$control
157		)
158		}
159		)
160	29x	} else if (x$control$interaction) {
161	10x	Map(
162	10x	mod = mod, covar = vars,
163	10x	f = function(mod, covar) {
164	22x	h_coxreg_extract_interaction(
165	22x	effect = x$vars$arm, covar = covar, mod = mod, data = x$data,
166	22x	at = x$at, control = x$control
167		)
168		}
169		)
170		} else {
171	14x	Map(
172	14x	mod = mod, vars = vars,
173	14x	f = function(mod, vars) {
174	36x	h_coxreg_univar_extract(
175	36x	effect = x$vars$arm, covar = vars, data = x$data, mod = mod,
176	36x	control = x$control
177		)
178		}
179		)
180		}
181	29x	result <- do.call(rbind, result)
182
183	29x	result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
184	29x	result$n <- lapply(result$n, empty_vector_if_na)
185	29x	result$ci <- lapply(result$ci, empty_vector_if_na)
186	29x	result$hr <- lapply(result$hr, empty_vector_if_na)
187	29x	if (x$control$interaction) {
188	10x	result$pval_inter <- lapply(result$pval_inter, empty_vector_if_na)
189		# Remove interaction p-values due to change in specifications.
190	10x	result$pval[result$effect != "Treatment:"] <- NA
191		}
192	29x	result$pval <- lapply(result$pval, empty_vector_if_na)
193	29x	attr(result, "conf_level") <- x$control$conf_level
194	29x	result
195		}
196
197		#' @describeIn tidy_coxreg Custom tidy method for a multivariate Cox regression.
198		#'
199		#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_multivar()].
200		#'
201		#' @method tidy coxreg.multivar
202		#'
203		#' @examples
204		#' multivar_model <- fit_coxreg_multivar(
205		#' variables = list(
206		#' time = "time", event = "status", arm = "armcd",
207		#' covariates = c("covar1", "covar2")
208		#' ),
209		#' data = dta_bladder
210		#' )
211		#' broom::tidy(multivar_model)
212		#'
213		#' @export
214		tidy.coxreg.multivar <- function(x, # nolint
215		...) {
216	8x	checkmate::assert_class(x, "coxreg.multivar")
217	8x	vars <- c(x$vars$arm, x$vars$covariates)
218
219		# Convert the model summaries to data.
220	8x	result <- Map(
221	8x	vars = vars,
222	8x	f = function(vars) {
223	28x	h_coxreg_multivar_extract(
224	28x	var = vars, data = x$data,
225	28x	mod = x$mod, control = x$control
226		)
227		}
228		)
229	8x	result <- do.call(rbind, result)
230
231	8x	result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
232	8x	result$ci <- lapply(result$ci, empty_vector_if_na)
233	8x	result$hr <- lapply(result$hr, empty_vector_if_na)
234	8x	result$pval <- lapply(result$pval, empty_vector_if_na)
235	8x	result <- result[, names(result) != "n"]
236	8x	attr(result, "conf_level") <- x$control$conf_level
237
238	8x	result
239		}
240
241		#' Fits for Cox Proportional Hazards Regression
242		#'
243		#' @description `r lifecycle::badge("stable")`
244		#'
245		#' Fitting functions for univariate and multivariate Cox regression models.
246		#'
247		#' @param variables (`list`)\cr a named list corresponds to the names of variables found in `data`, passed as a named
248		#' list and corresponding to `time`, `event`, `arm`, `strata`, and `covariates` terms. If `arm` is missing from
249		#' `variables`, then only Cox model(s) including the `covariates` will be fitted and the corresponding effect
250		#' estimates will be tabulated later.
251		#' @param data (`data.frame`)\cr the dataset containing the variables to fit the models.
252		#' @param at (`list` of `numeric`)\cr when the candidate covariate is a `numeric`, use `at` to specify
253		#' the value of the covariate at which the effect should be estimated.
254		#' @param control (`list`)\cr a list of parameters as returned by the helper function [control_coxreg()].
255		#'
256		#' @seealso [h_cox_regression] for relevant helper functions, [cox_regression].
257		#'
258		#' @examples
259		#' library(survival)
260		#'
261		#' set.seed(1, kind = "Mersenne-Twister")
262		#'
263		#' # Testing dataset [survival::bladder].
264		#' dta_bladder <- with(
265		#' data = bladder[bladder$enum < 5, ],
266		#' data.frame(
267		#' time = stop,
268		#' status = event,
269		#' armcd = as.factor(rx),
270		#' covar1 = as.factor(enum),
271		#' covar2 = factor(
272		#' sample(as.factor(enum)),
273		#' levels = 1:4, labels = c("F", "F", "M", "M")
274		#' )
275		#' )
276		#' )
277		#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
278		#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
279		#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
280		#'
281		#' plot(
282		#' survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
283		#' lty = 2:4,
284		#' xlab = "Months",
285		#' col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
286		#' )
287		#'
288		#' @name fit_coxreg
289		NULL
290
291		#' @describeIn fit_coxreg Fit a series of univariate Cox regression models given the inputs.
292		#'
293		#' @return
294		#' * `fit_coxreg_univar()` returns a `coxreg.univar` class object which is a named `list`
295		#' with 5 elements:
296		#' * `mod`: Cox regression models fitted by [survival::coxph()].
297		#' * `data`: The original data frame input.
298		#' * `control`: The original control input.
299		#' * `vars`: The variables used in the model.
300		#' * `at`: Value of the covariate at which the effect should be estimated.
301		#'
302		#' @note When using `fit_coxreg_univar` there should be two study arms.
303		#'
304		#' @examples
305		#' # fit_coxreg_univar
306		#'
307		#' ## Cox regression: arm + 1 covariate.
308		#' mod1 <- fit_coxreg_univar(
309		#' variables = list(
310		#' time = "time", event = "status", arm = "armcd",
311		#' covariates = "covar1"
312		#' ),
313		#' data = dta_bladder,
314		#' control = control_coxreg(conf_level = 0.91)
315		#' )
316		#'
317		#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
318		#' mod2 <- fit_coxreg_univar(
319		#' variables = list(
320		#' time = "time", event = "status", arm = "armcd",
321		#' covariates = c("covar1", "covar2")
322		#' ),
323		#' data = dta_bladder,
324		#' control = control_coxreg(conf_level = 0.91, interaction = TRUE)
325		#' )
326		#'
327		#' ## Cox regression: arm + 1 covariate, stratified analysis.
328		#' mod3 <- fit_coxreg_univar(
329		#' variables = list(
330		#' time = "time", event = "status", arm = "armcd", strata = "covar2",
331		#' covariates = c("covar1")
332		#' ),
333		#' data = dta_bladder,
334		#' control = control_coxreg(conf_level = 0.91)
335		#' )
336		#'
337		#' ## Cox regression: no arm, only covariates.
338		#' mod4 <- fit_coxreg_univar(
339		#' variables = list(
340		#' time = "time", event = "status",
341		#' covariates = c("covar1", "covar2")
342		#' ),
343		#' data = dta_bladder
344		#' )
345		#'
346		#' @export
347		fit_coxreg_univar <- function(variables,
348		data,
349		at = list(),
350		control = control_coxreg()) {
351	34x	checkmate::assert_list(variables, names = "named")
352	34x	has_arm <- "arm" %in% names(variables)
353	34x	arm_name <- if (has_arm) "arm" else NULL
354
355	34x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
356
357	34x	assert_df_with_variables(data, variables)
358	34x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
359
360	34x	if (!is.null(variables$strata)) {
361	4x	checkmate::assert_disjunct(control$pval_method, "likelihood")
362		}
363	33x	if (has_arm) {
364	27x	assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
365		}
366	32x	vars <- unlist(variables[c(arm_name, "covariates", "strata")], use.names = FALSE)
367	32x	for (i in vars) {
368	73x	if (is.factor(data[[i]])) {
369	63x	attr(data[[i]], "levels") <- levels(droplevels(data[[i]]))
370		}
371		}
372	32x	forms <- h_coxreg_univar_formulas(variables, interaction = control$interaction)
373	32x	mod <- lapply(
374	32x	forms, function(x) {
375	69x	survival::coxph(formula = stats::as.formula(x), data = data, ties = control$ties)
376		}
377		)
378	32x	structure(
379	32x	list(
380	32x	mod = mod,
381	32x	data = data,
382	32x	control = control,
383	32x	vars = variables,
384	32x	at = at
385		),
386	32x	class = "coxreg.univar"
387		)
388		}
389
390		#' @describeIn fit_coxreg Fit a multivariate Cox regression model.
391		#'
392		#' @return
393		#' * `fit_coxreg_multivar()` returns a `coxreg.multivar` class object which is a named list
394		#' with 4 elements:
395		#' * `mod`: Cox regression model fitted by [survival::coxph()].
396		#' * `data`: The original data frame input.
397		#' * `control`: The original control input.
398		#' * `vars`: The variables used in the model.
399		#'
400		#' @examples
401		#' # fit_coxreg_multivar
402		#'
403		#' ## Cox regression: multivariate Cox regression.
404		#' multivar_model <- fit_coxreg_multivar(
405		#' variables = list(
406		#' time = "time", event = "status", arm = "armcd",
407		#' covariates = c("covar1", "covar2")
408		#' ),
409		#' data = dta_bladder
410		#' )
411		#'
412		#' # Example without treatment arm.
413		#' multivar_covs_model <- fit_coxreg_multivar(
414		#' variables = list(
415		#' time = "time", event = "status",
416		#' covariates = c("covar1", "covar2")
417		#' ),
418		#' data = dta_bladder
419		#' )
420		#'
421		#' @export
422		fit_coxreg_multivar <- function(variables,
423		data,
424		control = control_coxreg()) {
425	63x	checkmate::assert_list(variables, names = "named")
426	63x	has_arm <- "arm" %in% names(variables)
427	63x	arm_name <- if (has_arm) "arm" else NULL
428
429	63x	if (!is.null(variables$covariates)) {
430	13x	checkmate::assert_character(variables$covariates)
431		}
432
433	63x	checkmate::assert_false(control$interaction)
434	63x	assert_df_with_variables(data, variables)
435	63x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
436
437	63x	if (!is.null(variables$strata)) {
438	3x	checkmate::assert_disjunct(control$pval_method, "likelihood")
439		}
440
441	62x	form <- h_coxreg_multivar_formula(variables)
442	62x	mod <- survival::coxph(
443	62x	formula = stats::as.formula(form),
444	62x	data = data,
445	62x	ties = control$ties
446		)
447	62x	structure(
448	62x	list(
449	62x	mod = mod,
450	62x	data = data,
451	62x	control = control,
452	62x	vars = variables
453		),
454	62x	class = "coxreg.multivar"
455		)
456		}
457
458		#' Muffled `car::Anova`
459		#'
460		#' Applied on survival models, [car::Anova()] signal that the `strata` terms is dropped from the model formula when
461		#' present, this function deliberately muffles this message.
462		#'
463		#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
464		#' @param test_statistic (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
465		#'
466		#' @return Returns the output of [car::Anova()], with convergence message muffled.
467		#'
468		#' @keywords internal
469		muffled_car_anova <- function(mod, test_statistic) {
470	154x	tryCatch(
471	154x	withCallingHandlers(
472	154x	expr = {
473	154x	car::Anova(
474	154x	mod,
475	154x	test.statistic = test_statistic,
476	154x	type = "III"
477		)
478		},
479	154x	message = function(m) invokeRestart("muffleMessage"),
480	154x	error = function(e) {
481	1x	stop(paste(
482	1x	"the model seems to have convergence problems, please try to change",
483	1x	"the configuration of covariates or strata variables, e.g.",
484	1x	"- original error:", e
485		))
486		}
487		)
488		)
489		}

1		#' Patient Counts for Laboratory Events (Worsen From Baseline) by Highest Grade Post-Baseline
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Patient count and fraction for laboratory events (worsen from baseline) shift table.
6		#'
7		#' @inheritParams argument_convention
8		#' @param variables (named `list` of `string`)\cr list of additional analysis variables including:
9		#' * `id` (`string`)\cr subject variable name.
10		#' * `baseline_var` (`string`)\cr name of the data column containing baseline toxicity variable.
11		#' * `direction_var` (`string`)\cr see `direction_var` for more details.
12		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("abnormal_by_worst_grade_worsen")`
13		#' to see all available statistics.
14		#'
15		#' @seealso Relevant helper functions [h_adlb_worsen()] and [h_worsen_counter()]
16		#'
17		#' @name abnormal_by_worst_grade_worsen
18		#' @order 1
19		NULL
20
21		#' Helper Function to Prepare `ADLB` with Worst Labs
22		#'
23		#' @description `r lifecycle::badge("stable")`
24		#'
25		#' Helper function to prepare a `df` for generate the patient count shift table
26		#'
27		#' @param adlb (`data.frame`)\cr `ADLB` dataframe
28		#' @param worst_flag_low (named `vector`)\cr Worst low post-baseline lab grade flag variable
29		#' @param worst_flag_high (named `vector`)\cr Worst high post-baseline lab grade flag variable
30		#' @param direction_var (`string`)\cr Direction variable specifying the direction of the shift table of interest.
31		#' Only lab records flagged by `L`, `H` or `B` are included in the shift table.
32		#' * `L`: low direction only
33		#' * `H`: high direction only
34		#' * `B`: both low and high directions
35		#'
36		#' @return `h_adlb_worsen()` returns the `adlb` `data.frame` containing only the
37		#' worst labs specified according to `worst_flag_low` or `worst_flag_high` for the
38		#' direction specified according to `direction_var`. For instance, for a lab that is
39		#' needed for the low direction only, only records flagged by `worst_flag_low` are
40		#' selected. For a lab that is needed for both low and high directions, the worst
41		#' low records are selected for the low direction, and the worst high record are selected
42		#' for the high direction.
43		#'
44		#' @seealso [abnormal_by_worst_grade_worsen]
45		#'
46		#' @examples
47		#' library(dplyr)
48		#'
49		#' # The direction variable, GRADDR, is based on metadata
50		#' adlb <- tern_ex_adlb %>%
51		#' mutate(
52		#' GRADDR = case_when(
53		#' PARAMCD == "ALT" ~ "B",
54		#' PARAMCD == "CRP" ~ "L",
55		#' PARAMCD == "IGA" ~ "H"
56		#' )
57		#' ) %>%
58		#' filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
59		#'
60		#' df <- h_adlb_worsen(
61		#' adlb,
62		#' worst_flag_low = c("WGRLOFL" = "Y"),
63		#' worst_flag_high = c("WGRHIFL" = "Y"),
64		#' direction_var = "GRADDR"
65		#' )
66		#'
67		#' @export
68		h_adlb_worsen <- function(adlb,
69		worst_flag_low = NULL,
70		worst_flag_high = NULL,
71		direction_var) {
72	5x	checkmate::assert_string(direction_var)
73	5x	checkmate::assert_subset(as.character(unique(adlb[[direction_var]])), c("B", "L", "H"))
74	5x	assert_df_with_variables(adlb, list("Col" = direction_var))
75
76	5x	if (any(unique(adlb[[direction_var]]) == "H")) {
77	4x	assert_df_with_variables(adlb, list("High" = names(worst_flag_high)))
78		}
79
80	5x	if (any(unique(adlb[[direction_var]]) == "L")) {
81	4x	assert_df_with_variables(adlb, list("Low" = names(worst_flag_low)))
82		}
83
84	5x	if (any(unique(adlb[[direction_var]]) == "B")) {
85	3x	assert_df_with_variables(
86	3x	adlb,
87	3x	list(
88	3x	"Low" = names(worst_flag_low),
89	3x	"High" = names(worst_flag_high)
90		)
91		)
92		}
93
94		# extract patients with worst post-baseline lab, either low or high or both
95	5x	worst_flag <- c(worst_flag_low, worst_flag_high)
96	5x	col_names <- names(worst_flag)
97	5x	filter_values <- worst_flag
98	5x	temp <- Map(
99	5x	function(x, y) which(adlb[[x]] == y),
100	5x	col_names,
101	5x	filter_values
102		)
103	5x	position_satisfy_filters <- Reduce(union, temp)
104
105		# select variables of interest
106	5x	adlb_f <- adlb[position_satisfy_filters, ]
107
108		# generate subsets for different directionality
109	5x	adlb_f_h <- adlb_f[which(adlb_f[[direction_var]] == "H"), ]
110	5x	adlb_f_l <- adlb_f[which(adlb_f[[direction_var]] == "L"), ]
111	5x	adlb_f_b <- adlb_f[which(adlb_f[[direction_var]] == "B"), ]
112
113		# for labs requiring both high and low, data is duplicated and will be stacked on top of each other
114	5x	adlb_f_b_h <- adlb_f_b
115	5x	adlb_f_b_l <- adlb_f_b
116
117		# extract data with worst lab
118	5x	if (!is.null(worst_flag_high) && !is.null(worst_flag_low)) {
119		# change H to High, L to Low
120	3x	adlb_f_h[[direction_var]] <- rep("High", nrow(adlb_f_h))
121	3x	adlb_f_l[[direction_var]] <- rep("Low", nrow(adlb_f_l))
122
123		# change, B to High and Low
124	3x	adlb_f_b_h[[direction_var]] <- rep("High", nrow(adlb_f_b_h))
125	3x	adlb_f_b_l[[direction_var]] <- rep("Low", nrow(adlb_f_b_l))
126
127	3x	adlb_out_h <- adlb_f_h[which(adlb_f_h[[names(worst_flag_high)]] == worst_flag_high), ]
128	3x	adlb_out_b_h <- adlb_f_b_h[which(adlb_f_b_h[[names(worst_flag_high)]] == worst_flag_high), ]
129	3x	adlb_out_l <- adlb_f_l[which(adlb_f_l[[names(worst_flag_low)]] == worst_flag_low), ]
130	3x	adlb_out_b_l <- adlb_f_b_l[which(adlb_f_b_l[[names(worst_flag_low)]] == worst_flag_low), ]
131
132	3x	out <- rbind(adlb_out_h, adlb_out_b_h, adlb_out_l, adlb_out_b_l)
133	2x	} else if (!is.null(worst_flag_high)) {
134	1x	adlb_f_h[[direction_var]] <- rep("High", nrow(adlb_f_h))
135	1x	adlb_f_b_h[[direction_var]] <- rep("High", nrow(adlb_f_b_h))
136
137	1x	adlb_out_h <- adlb_f_h[which(adlb_f_h[[names(worst_flag_high)]] == worst_flag_high), ]
138	1x	adlb_out_b_h <- adlb_f_b_h[which(adlb_f_b_h[[names(worst_flag_high)]] == worst_flag_high), ]
139
140	1x	out <- rbind(adlb_out_h, adlb_out_b_h)
141	1x	} else if (!is.null(worst_flag_low)) {
142	1x	adlb_f_l[[direction_var]] <- rep("Low", nrow(adlb_f_l))
143	1x	adlb_f_b_l[[direction_var]] <- rep("Low", nrow(adlb_f_b_l))
144
145	1x	adlb_out_l <- adlb_f_l[which(adlb_f_l[[names(worst_flag_low)]] == worst_flag_low), ]
146	1x	adlb_out_b_l <- adlb_f_b_l[which(adlb_f_b_l[[names(worst_flag_low)]] == worst_flag_low), ]
147
148	1x	out <- rbind(adlb_out_l, adlb_out_b_l)
149		}
150
151		# label
152	5x	formatters::var_labels(out) <- formatters::var_labels(adlb_f, fill = FALSE)
153		# NA
154	5x	out
155		}
156
157		#' Helper Function to Analyze Patients for [s_count_abnormal_lab_worsen_by_baseline()]
158		#'
159		#' @description `r lifecycle::badge("stable")`
160		#'
161		#' Helper function to count the number of patients and the fraction of patients according to
162		#' highest post-baseline lab grade variable `.var`, baseline lab grade variable `baseline_var`,
163		#' and the direction of interest specified in `direction_var`.
164		#'
165		#' @inheritParams argument_convention
166		#' @inheritParams h_adlb_worsen
167		#' @param baseline_var (`string`)\cr baseline lab grade variable
168		#'
169		#' @return `h_worsen_counter()` returns the counts and fraction of patients
170		#' whose worst post-baseline lab grades are worse than their baseline grades, for
171		#' post-baseline worst grades "1", "2", "3", "4" and "Any".
172		#'
173		#' @seealso [abnormal_by_worst_grade_worsen]
174		#'
175		#' @examples
176		#' library(dplyr)
177		#'
178		#' # The direction variable, GRADDR, is based on metadata
179		#' adlb <- tern_ex_adlb %>%
180		#' mutate(
181		#' GRADDR = case_when(
182		#' PARAMCD == "ALT" ~ "B",
183		#' PARAMCD == "CRP" ~ "L",
184		#' PARAMCD == "IGA" ~ "H"
185		#' )
186		#' ) %>%
187		#' filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
188		#'
189		#' df <- h_adlb_worsen(
190		#' adlb,
191		#' worst_flag_low = c("WGRLOFL" = "Y"),
192		#' worst_flag_high = c("WGRHIFL" = "Y"),
193		#' direction_var = "GRADDR"
194		#' )
195		#'
196		#' # `h_worsen_counter`
197		#' h_worsen_counter(
198		#' df %>% filter(PARAMCD == "CRP" & GRADDR == "Low"),
199		#' id = "USUBJID",
200		#' .var = "ATOXGR",
201		#' baseline_var = "BTOXGR",
202		#' direction_var = "GRADDR"
203		#' )
204		#'
205		#' @export
206		h_worsen_counter <- function(df, id, .var, baseline_var, direction_var) {
207	17x	checkmate::assert_string(id)
208	17x	checkmate::assert_string(.var)
209	17x	checkmate::assert_string(baseline_var)
210	17x	checkmate::assert_scalar(unique(df[[direction_var]]))
211	17x	checkmate::assert_subset(unique(df[[direction_var]]), c("High", "Low"))
212	17x	assert_df_with_variables(df, list(val = c(id, .var, baseline_var, direction_var)))
213
214		# remove post-baseline missing
215	17x	df <- df[df[[.var]] != "<Missing>", ]
216
217		# obtain directionality
218	17x	direction <- unique(df[[direction_var]])
219
220	17x	if (direction == "Low") {
221	10x	grade <- -1:-4
222	10x	worst_grade <- -4
223	7x	} else if (direction == "High") {
224	7x	grade <- 1:4
225	7x	worst_grade <- 4
226		}
227
228	17x	if (nrow(df) > 0) {
229	17x	by_grade <- lapply(grade, function(i) {
230		# filter baseline values that is less than i or <Missing>
231	68x	df_temp <- df[df[[baseline_var]] %in% c((i + sign(i) * -1):(-1 * worst_grade), "<Missing>"), ]
232		# num: number of patients with post-baseline worst lab equal to i
233	68x	num <- length(unique(df_temp[df_temp[[.var]] %in% i, id, drop = TRUE]))
234		# denom: number of patients with baseline values less than i or <missing> and post-baseline in the same direction
235	68x	denom <- length(unique(df_temp[[id]]))
236	68x	rm(df_temp)
237	68x	c(num = num, denom = denom)
238		})
239		} else {
240	!	by_grade <- lapply(1, function(i) {
241	!	c(num = 0, denom = 0)
242		})
243		}
244
245	17x	names(by_grade) <- as.character(seq_along(by_grade))
246
247		# baseline grade less 4 or missing
248	17x	df_temp <- df[!df[[baseline_var]] %in% worst_grade, ]
249
250		# denom: number of patients with baseline values less than 4 or <missing> and post-baseline in the same direction
251	17x	denom <- length(unique(df_temp[, id, drop = TRUE]))
252
253		# condition 1: missing baseline and in the direction of abnormality
254	17x	con1 <- which(df_temp[[baseline_var]] == "<Missing>" & df_temp[[.var]] %in% grade)
255	17x	df_temp_nm <- df_temp[which(df_temp[[baseline_var]] != "<Missing>" & df_temp[[.var]] %in% grade), ]
256
257		# condition 2: if post-baseline values are present then post-baseline values must be worse than baseline
258	17x	if (direction == "Low") {
259	10x	con2 <- which(as.numeric(as.character(df_temp_nm[[.var]])) < as.numeric(as.character(df_temp_nm[[baseline_var]])))
260		} else {
261	7x	con2 <- which(as.numeric(as.character(df_temp_nm[[.var]])) > as.numeric(as.character(df_temp_nm[[baseline_var]])))
262		}
263
264		# number of patients satisfy either conditions 1 or 2
265	17x	num <- length(unique(df_temp[union(con1, con2), id, drop = TRUE]))
266
267	17x	list(fraction = c(by_grade, list("Any" = c(num = num, denom = denom))))
268		}
269
270		#' @describeIn abnormal_by_worst_grade_worsen Statistics function for patients whose worst post-baseline
271		#' lab grades are worse than their baseline grades.
272		#'
273		#' @return
274		#' * `s_count_abnormal_lab_worsen_by_baseline()` returns the counts and fraction of patients whose worst
275		#' post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades
276		#' "1", "2", "3", "4" and "Any".
277		#'
278		#' @keywords internal
279		s_count_abnormal_lab_worsen_by_baseline <- function(df, # nolint
280		.var = "ATOXGR",
281		variables = list(
282		id = "USUBJID",
283		baseline_var = "BTOXGR",
284		direction_var = "GRADDR"
285		)) {
286	1x	checkmate::assert_string(.var)
287	1x	checkmate::assert_set_equal(names(variables), c("id", "baseline_var", "direction_var"))
288	1x	checkmate::assert_string(variables$id)
289	1x	checkmate::assert_string(variables$baseline_var)
290	1x	checkmate::assert_string(variables$direction_var)
291	1x	assert_df_with_variables(df, c(aval = .var, variables[1:3]))
292	1x	assert_list_of_variables(variables)
293
294	1x	h_worsen_counter(df, variables$id, .var, variables$baseline_var, variables$direction_var)
295		}
296
297
298		#' @describeIn abnormal_by_worst_grade_worsen Formatted analysis function which is used as `afun`
299		#' in `count_abnormal_lab_worsen_by_baseline()`.
300		#'
301		#' @return
302		#' * `a_count_abnormal_lab_worsen_by_baseline()` returns the corresponding list with
303		#' formatted [rtables::CellValue()].
304		#'
305		#' @keywords internal
306		a_count_abnormal_lab_worsen_by_baseline <- make_afun( # nolint
307		s_count_abnormal_lab_worsen_by_baseline,
308		.formats = c(fraction = format_fraction),
309		.ungroup_stats = "fraction"
310		)
311
312		#' @describeIn abnormal_by_worst_grade_worsen Layout-creating function which can take statistics function
313		#' arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
314		#'
315		#' @return
316		#' * `count_abnormal_lab_worsen_by_baseline()` returns a layout object suitable for passing to further layouting
317		#' functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted
318		#' rows containing the statistics from `s_count_abnormal_lab_worsen_by_baseline()` to the table layout.
319		#'
320		#' @examples
321		#' library(dplyr)
322		#'
323		#' # The direction variable, GRADDR, is based on metadata
324		#' adlb <- tern_ex_adlb %>%
325		#' mutate(
326		#' GRADDR = case_when(
327		#' PARAMCD == "ALT" ~ "B",
328		#' PARAMCD == "CRP" ~ "L",
329		#' PARAMCD == "IGA" ~ "H"
330		#' )
331		#' ) %>%
332		#' filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
333		#'
334		#' df <- h_adlb_worsen(
335		#' adlb,
336		#' worst_flag_low = c("WGRLOFL" = "Y"),
337		#' worst_flag_high = c("WGRHIFL" = "Y"),
338		#' direction_var = "GRADDR"
339		#' )
340		#'
341		#' basic_table() %>%
342		#' split_cols_by("ARMCD") %>%
343		#' add_colcounts() %>%
344		#' split_rows_by("PARAMCD") %>%
345		#' split_rows_by("GRADDR") %>%
346		#' count_abnormal_lab_worsen_by_baseline(
347		#' var = "ATOXGR",
348		#' variables = list(
349		#' id = "USUBJID",
350		#' baseline_var = "BTOXGR",
351		#' direction_var = "GRADDR"
352		#' )
353		#' ) %>%
354		#' append_topleft("Direction of Abnormality") %>%
355		#' build_table(df = df, alt_counts_df = tern_ex_adsl)
356		#'
357		#' @export
358		#' @order 2
359		count_abnormal_lab_worsen_by_baseline <- function(lyt, # nolint
360		var,
361		variables = list(
362		id = "USUBJID",
363		baseline_var = "BTOXGR",
364		direction_var = "GRADDR"
365		),
366		na_str = default_na_str(),
367		nested = TRUE,
368		...,
369		table_names = NULL,
370		.stats = NULL,
371		.formats = NULL,
372		.labels = NULL,
373		.indent_mods = NULL) {
374	1x	checkmate::assert_string(var)
375
376	1x	extra_args <- list(variables = variables, ...)
377
378	1x	afun <- make_afun(
379	1x	a_count_abnormal_lab_worsen_by_baseline,
380	1x	.stats = .stats,
381	1x	.formats = .formats,
382	1x	.labels = .labels,
383	1x	.indent_mods = .indent_mods
384		)
385
386	1x	lyt <- analyze(
387	1x	lyt = lyt,
388	1x	vars = var,
389	1x	afun = afun,
390	1x	na_str = na_str,
391	1x	nested = nested,
392	1x	extra_args = extra_args,
393	1x	show_labels = "hidden"
394		)
395
396	1x	lyt
397		}

1		#' Helper Functions for Tabulating Biomarker Effects on Survival by Subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions which are documented here separately to not confuse the user
6		#' when reading about the user-facing functions.
7		#'
8		#' @inheritParams survival_biomarkers_subgroups
9		#' @inheritParams argument_convention
10		#' @inheritParams fit_coxreg_multivar
11		#'
12		#' @examples
13		#' library(dplyr)
14		#' library(forcats)
15		#'
16		#' adtte <- tern_ex_adtte
17		#'
18		#' # Save variable labels before data processing steps.
19		#' adtte_labels <- formatters::var_labels(adtte, fill = FALSE)
20		#'
21		#' adtte_f <- adtte %>%
22		#' filter(PARAMCD == "OS") %>%
23		#' mutate(
24		#' AVALU = as.character(AVALU),
25		#' is_event = CNSR == 0
26		#' )
27		#' labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
28		#' formatters::var_labels(adtte_f)[names(labels)] <- labels
29		#'
30		#' @name h_survival_biomarkers_subgroups
31		NULL
32
33		#' @describeIn h_survival_biomarkers_subgroups helps with converting the "survival" function variable list
34		#' to the "Cox regression" variable list. The reason is that currently there is an inconsistency between the variable
35		#' names accepted by `extract_survival_subgroups()` and `fit_coxreg_multivar()`.
36		#'
37		#' @param biomarker (`string`)\cr the name of the biomarker variable.
38		#'
39		#' @return
40		#' * `h_surv_to_coxreg_variables()` returns a named `list` of elements `time`, `event`, `arm`,
41		#' `covariates`, and `strata`.
42		#'
43		#' @examples
44		#' # This is how the variable list is converted internally.
45		#' h_surv_to_coxreg_variables(
46		#' variables = list(
47		#' tte = "AVAL",
48		#' is_event = "EVNT",
49		#' covariates = c("A", "B"),
50		#' strata = "D"
51		#' ),
52		#' biomarker = "AGE"
53		#' )
54		#'
55		#' @export
56		h_surv_to_coxreg_variables <- function(variables, biomarker) {
57	53x	checkmate::assert_list(variables)
58	53x	checkmate::assert_string(variables$tte)
59	53x	checkmate::assert_string(variables$is_event)
60	53x	checkmate::assert_string(biomarker)
61	53x	list(
62	53x	time = variables$tte,
63	53x	event = variables$is_event,
64	53x	arm = biomarker,
65	53x	covariates = variables$covariates,
66	53x	strata = variables$strata
67		)
68		}
69
70		#' @describeIn h_survival_biomarkers_subgroups prepares estimates for number of events, patients and median survival
71		#' times, as well as hazard ratio estimates, confidence intervals and p-values, for multiple biomarkers
72		#' in a given single data set.
73		#' `variables` corresponds to names of variables found in `data`, passed as a named list and requires elements
74		#' `tte`, `is_event`, `biomarkers` (vector of continuous biomarker variables) and optionally `subgroups` and `strata`.
75		#'
76		#' @return
77		#' * `h_coxreg_mult_cont_df()` returns a `data.frame` containing estimates and statistics for the selected biomarkers.
78		#'
79		#' @examples
80		#' # For a single population, estimate separately the effects
81		#' # of two biomarkers.
82		#' df <- h_coxreg_mult_cont_df(
83		#' variables = list(
84		#' tte = "AVAL",
85		#' is_event = "is_event",
86		#' biomarkers = c("BMRKR1", "AGE"),
87		#' covariates = "SEX",
88		#' strata = c("STRATA1", "STRATA2")
89		#' ),
90		#' data = adtte_f
91		#' )
92		#' df
93		#'
94		#' # If the data set is empty, still the corresponding rows with missings are returned.
95		#' h_coxreg_mult_cont_df(
96		#' variables = list(
97		#' tte = "AVAL",
98		#' is_event = "is_event",
99		#' biomarkers = c("BMRKR1", "AGE"),
100		#' covariates = "REGION1",
101		#' strata = c("STRATA1", "STRATA2")
102		#' ),
103		#' data = adtte_f[NULL, ]
104		#' )
105		#'
106		#' @export
107		h_coxreg_mult_cont_df <- function(variables,
108		data,
109		control = control_coxreg()) {
110	27x	if ("strat" %in% names(variables)) {
111	!	warning(
112	!	"Warning: the `strat` element name of the `variables` list argument to `h_coxreg_mult_cont_df() ",
113	!	"was deprecated in tern 0.9.3.\n ",
114	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
115		)
116	!	variables[["strata"]] <- variables[["strat"]]
117		}
118
119	27x	assert_df_with_variables(data, variables)
120	27x	checkmate::assert_list(control, names = "named")
121	27x	checkmate::assert_character(variables$biomarkers, min.len = 1, any.missing = FALSE)
122	27x	conf_level <- control[["conf_level"]]
123	27x	pval_label <- paste0(
124		# the regex capitalizes the first letter of the string / senetence.
125	27x	"p-value (", gsub("(^[a-z])", "\\U\\1", trimws(control[["pval_method"]]), perl = TRUE), ")"
126		)
127		# If there is any data, run model, otherwise return empty results.
128	27x	if (nrow(data) > 0) {
129	26x	bm_cols <- match(variables$biomarkers, names(data))
130	26x	l_result <- lapply(variables$biomarkers, function(bm) {
131	52x	coxreg_list <- fit_coxreg_multivar(
132	52x	variables = h_surv_to_coxreg_variables(variables, bm),
133	52x	data = data,
134	52x	control = control
135		)
136	52x	result <- do.call(
137	52x	h_coxreg_multivar_extract,
138	52x	c(list(var = bm), coxreg_list[c("mod", "data", "control")])
139		)
140	52x	data_fit <- as.data.frame(as.matrix(coxreg_list$mod$y))
141	52x	data_fit$status <- as.logical(data_fit$status)
142	52x	median <- s_surv_time(
143	52x	df = data_fit,
144	52x	.var = "time",
145	52x	is_event = "status"
146	52x	)$median
147	52x	data.frame(
148		# Dummy column needed downstream to create a nested header.
149	52x	biomarker = bm,
150	52x	biomarker_label = formatters::var_labels(data[bm], fill = TRUE),
151	52x	n_tot = coxreg_list$mod$n,
152	52x	n_tot_events = coxreg_list$mod$nevent,
153	52x	median = as.numeric(median),
154	52x	result[1L, c("hr", "lcl", "ucl")],
155	52x	conf_level = conf_level,
156	52x	pval = result[1L, "pval"],
157	52x	pval_label = pval_label,
158	52x	stringsAsFactors = FALSE
159		)
160		})
161	26x	do.call(rbind, args = c(l_result, make.row.names = FALSE))
162		} else {
163	1x	data.frame(
164	1x	biomarker = variables$biomarkers,
165	1x	biomarker_label = formatters::var_labels(data[variables$biomarkers], fill = TRUE),
166	1x	n_tot = 0L,
167	1x	n_tot_events = 0L,
168	1x	median = NA,
169	1x	hr = NA,
170	1x	lcl = NA,
171	1x	ucl = NA,
172	1x	conf_level = conf_level,
173	1x	pval = NA,
174	1x	pval_label = pval_label,
175	1x	row.names = seq_along(variables$biomarkers),
176	1x	stringsAsFactors = FALSE
177		)
178		}
179		}
180
181		#' @describeIn h_survival_biomarkers_subgroups prepares a single sub-table given a `df_sub` containing
182		#' the results for a single biomarker.
183		#'
184		#' @param df (`data.frame`)\cr results for a single biomarker, as part of what is
185		#' returned by [extract_survival_biomarkers()] (it needs a couple of columns which are
186		#' added by that high-level function relative to what is returned by [h_coxreg_mult_cont_df()],
187		#' see the example).
188		#'
189		#' @return
190		#' * `h_tab_surv_one_biomarker()` returns an `rtables` table object with the given statistics arranged in columns.
191		#'
192		#' @examples
193		#' # Starting from above `df`, zoom in on one biomarker and add required columns.
194		#' df1 <- df[1, ]
195		#' df1$subgroup <- "All patients"
196		#' df1$row_type <- "content"
197		#' df1$var <- "ALL"
198		#' df1$var_label <- "All patients"
199		#' h_tab_surv_one_biomarker(
200		#' df1,
201		#' vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
202		#' time_unit = "days"
203		#' )
204		#'
205		#' @export
206		h_tab_surv_one_biomarker <- function(df,
207		vars,
208		time_unit,
209		na_str = default_na_str(),
210		.indent_mods = 0L,
211		...) {
212	8x	afuns <- a_survival_subgroups(na_str = na_str)[vars]
213	8x	colvars <- d_survival_subgroups_colvars(
214	8x	vars,
215	8x	conf_level = df$conf_level[1],
216	8x	method = df$pval_label[1],
217	8x	time_unit = time_unit
218		)
219	8x	h_tab_one_biomarker(
220	8x	df = df,
221	8x	afuns = afuns,
222	8x	colvars = colvars,
223	8x	na_str = na_str,
224	8x	.indent_mods = .indent_mods,
225		...
226		)
227		}

1		#' Control Function for `CoxPH` Model
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' This is an auxiliary function for controlling arguments for `CoxPH` model, typically used internally to specify
6		#' details of `CoxPH` model for [s_coxph_pairwise()]. `conf_level` refers to Hazard Ratio estimation.
7		#'
8		#' @inheritParams argument_convention
9		#' @param pval_method (`string`)\cr p-value method for testing hazard ratio = 1.
10		#' Default method is `"log-rank"`, can also be set to `"wald"` or `"likelihood"`.
11		#' @param ties (`string`)\cr specifying the method for tie handling. Default is `"efron"`,
12		#' can also be set to `"breslow"` or `"exact"`. See more in [survival::coxph()].
13		#'
14		#' @return A list of components with the same names as the arguments
15		#'
16		#' @export
17		control_coxph <- function(pval_method = c("log-rank", "wald", "likelihood"),
18		ties = c("efron", "breslow", "exact"),
19		conf_level = 0.95) {
20	44x	pval_method <- match.arg(pval_method)
21	43x	ties <- match.arg(ties)
22	43x	assert_proportion_value(conf_level)
23
24	42x	list(pval_method = pval_method, ties = ties, conf_level = conf_level)
25		}
26
27		#' Control Function for `survfit` Model for Survival Time
28		#'
29		#' @description `r lifecycle::badge("stable")`
30		#'
31		#' This is an auxiliary function for controlling arguments for `survfit` model, typically used internally to specify
32		#' details of `survfit` model for [s_surv_time()]. `conf_level` refers to survival time estimation.
33		#'
34		#' @inheritParams argument_convention
35		#' @param conf_type (`string`)\cr confidence interval type. Options are "plain" (default), "log", "log-log",
36		#' see more in [survival::survfit()]. Note option "none" is no longer supported.
37		#' @param quantiles (`numeric`)\cr of length two to specify the quantiles of survival time.
38		#'
39		#' @return A list of components with the same names as the arguments
40		#'
41		#' @export
42		control_surv_time <- function(conf_level = 0.95,
43		conf_type = c("plain", "log", "log-log"),
44		quantiles = c(0.25, 0.75)) {
45	179x	conf_type <- match.arg(conf_type)
46	178x	checkmate::assert_numeric(quantiles, lower = 0, upper = 1, len = 2, unique = TRUE, sorted = TRUE)
47	177x	nullo <- lapply(quantiles, assert_proportion_value)
48	177x	assert_proportion_value(conf_level)
49	176x	list(conf_level = conf_level, conf_type = conf_type, quantiles = quantiles)
50		}
51
52		#' Control Function for `survfit` Model for Patient's Survival Rate at time point
53		#'
54		#' @description `r lifecycle::badge("stable")`
55		#'
56		#' This is an auxiliary function for controlling arguments for `survfit` model, typically used internally to specify
57		#' details of `survfit` model for [s_surv_timepoint()]. `conf_level` refers to patient risk estimation at a time point.
58		#'
59		#' @inheritParams argument_convention
60		#' @inheritParams control_surv_time
61		#'
62		#' @return A list of components with the same names as the arguments
63		#'
64		#' @export
65		control_surv_timepoint <- function(conf_level = 0.95,
66		conf_type = c("plain", "log", "log-log")) {
67	17x	conf_type <- match.arg(conf_type)
68	16x	assert_proportion_value(conf_level)
69	15x	list(
70	15x	conf_level = conf_level,
71	15x	conf_type = conf_type
72		)
73		}

1		#' Odds Ratio Estimation
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Compares bivariate responses between two groups in terms of odds ratios
6		#' along with a confidence interval.
7		#'
8		#' @inheritParams split_cols_by_groups
9		#' @inheritParams argument_convention
10		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_odds_ratio")`
11		#' to see available statistics for this function.
12		#'
13		#' @details This function uses either logistic regression for unstratified
14		#' analyses, or conditional logistic regression for stratified analyses.
15		#' The Wald confidence interval with the specified confidence level is
16		#' calculated.
17		#'
18		#' @note For stratified analyses, there is currently no implementation for conditional
19		#' likelihood confidence intervals, therefore the likelihood confidence interval is not
20		#' yet available as an option. Besides, when `rsp` contains only responders or non-responders,
21		#' then the result values will be `NA`, because no odds ratio estimation is possible.
22		#'
23		#' @seealso Relevant helper function [h_odds_ratio()].
24		#'
25		#' @name odds_ratio
26		#' @order 1
27		NULL
28
29		#' @describeIn odds_ratio Statistics function which estimates the odds ratio
30		#' between a treatment and a control. A `variables` list with `arm` and `strata`
31		#' variable names must be passed if a stratified analysis is required.
32		#'
33		#' @return
34		#' * `s_odds_ratio()` returns a named list with the statistics `or_ci`
35		#' (containing `est`, `lcl`, and `ucl`) and `n_tot`.
36		#'
37		#' @examples
38		#' # Unstratified analysis.
39		#' s_odds_ratio(
40		#' df = subset(dta, grp == "A"),
41		#' .var = "rsp",
42		#' .ref_group = subset(dta, grp == "B"),
43		#' .in_ref_col = FALSE,
44		#' .df_row = dta
45		#' )
46		#'
47		#' # Stratified analysis.
48		#' s_odds_ratio(
49		#' df = subset(dta, grp == "A"),
50		#' .var = "rsp",
51		#' .ref_group = subset(dta, grp == "B"),
52		#' .in_ref_col = FALSE,
53		#' .df_row = dta,
54		#' variables = list(arm = "grp", strata = "strata")
55		#' )
56		#'
57		#' @export
58		s_odds_ratio <- function(df,
59		.var,
60		.ref_group,
61		.in_ref_col,
62		.df_row,
63		variables = list(arm = NULL, strata = NULL),
64		conf_level = 0.95,
65		groups_list = NULL) {
66	70x	y <- list(or_ci = "", n_tot = "")
67
68	70x	if (!.in_ref_col) {
69	70x	assert_proportion_value(conf_level)
70	70x	assert_df_with_variables(df, list(rsp = .var))
71	70x	assert_df_with_variables(.ref_group, list(rsp = .var))
72
73	70x	if (is.null(variables$strata)) {
74	57x	data <- data.frame(
75	57x	rsp = c(.ref_group[[.var]], df[[.var]]),
76	57x	grp = factor(
77	57x	rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
78	57x	levels = c("ref", "Not-ref")
79		)
80		)
81	57x	y <- or_glm(data, conf_level = conf_level)
82		} else {
83	13x	assert_df_with_variables(.df_row, c(list(rsp = .var), variables))
84
85		# The group variable prepared for clogit must be synchronised with combination groups definition.
86	13x	if (is.null(groups_list)) {
87	12x	ref_grp <- as.character(unique(.ref_group[[variables$arm]]))
88	12x	trt_grp <- as.character(unique(df[[variables$arm]]))
89	12x	grp <- stats::relevel(factor(.df_row[[variables$arm]]), ref = ref_grp)
90		} else {
91		# If more than one level in reference col.
92	1x	reference <- as.character(unique(.ref_group[[variables$arm]]))
93	1x	grp_ref_flag <- vapply(
94	1x	X = groups_list,
95	1x	FUN.VALUE = TRUE,
96	1x	FUN = function(x) all(reference %in% x)
97		)
98	1x	ref_grp <- names(groups_list)[grp_ref_flag]
99
100		# If more than one level in treatment col.
101	1x	treatment <- as.character(unique(df[[variables$arm]]))
102	1x	grp_trt_flag <- vapply(
103	1x	X = groups_list,
104	1x	FUN.VALUE = TRUE,
105	1x	FUN = function(x) all(treatment %in% x)
106		)
107	1x	trt_grp <- names(groups_list)[grp_trt_flag]
108
109	1x	grp <- combine_levels(.df_row[[variables$arm]], levels = reference, new_level = ref_grp)
110	1x	grp <- combine_levels(grp, levels = treatment, new_level = trt_grp)
111		}
112
113		# The reference level in `grp` must be the same as in the `rtables` column split.
114	13x	data <- data.frame(
115	13x	rsp = .df_row[[.var]],
116	13x	grp = grp,
117	13x	strata = interaction(.df_row[variables$strata])
118		)
119	13x	y_all <- or_clogit(data, conf_level = conf_level)
120	13x	checkmate::assert_string(trt_grp)
121	13x	checkmate::assert_subset(trt_grp, names(y_all$or_ci))
122	12x	y$or_ci <- y_all$or_ci[[trt_grp]]
123	12x	y$n_tot <- y_all$n_tot
124		}
125		}
126
127	69x	y$or_ci <- formatters::with_label(
128	69x	x = y$or_ci,
129	69x	label = paste0("Odds Ratio (", 100 * conf_level, "% CI)")
130		)
131
132	69x	y$n_tot <- formatters::with_label(
133	69x	x = y$n_tot,
134	69x	label = "Total n"
135		)
136
137	69x	y
138		}
139
140		#' @describeIn odds_ratio Formatted analysis function which is used as `afun` in `estimate_odds_ratio()`.
141		#'
142		#' @return
143		#' * `a_odds_ratio()` returns the corresponding list with formatted [rtables::CellValue()].
144		#'
145		#' @examples
146		#' a_odds_ratio(
147		#' df = subset(dta, grp == "A"),
148		#' .var = "rsp",
149		#' .ref_group = subset(dta, grp == "B"),
150		#' .in_ref_col = FALSE,
151		#' .df_row = dta
152		#' )
153		#'
154		#' @export
155		a_odds_ratio <- make_afun(
156		s_odds_ratio,
157		.formats = c(or_ci = "xx.xx (xx.xx - xx.xx)"),
158		.indent_mods = c(or_ci = 1L)
159		)
160
161		#' @describeIn odds_ratio Layout-creating function which can take statistics function arguments
162		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
163		#'
164		#' @param ... arguments passed to `s_odds_ratio()`.
165		#'
166		#' @return
167		#' * `estimate_odds_ratio()` returns a layout object suitable for passing to further layouting functions,
168		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
169		#' the statistics from `s_odds_ratio()` to the table layout.
170		#'
171		#' @examples
172		#' set.seed(12)
173		#' dta <- data.frame(
174		#' rsp = sample(c(TRUE, FALSE), 100, TRUE),
175		#' grp = factor(rep(c("A", "B"), each = 50), levels = c("A", "B")),
176		#' strata = factor(sample(c("C", "D"), 100, TRUE))
177		#' )
178		#'
179		#' l <- basic_table() %>%
180		#' split_cols_by(var = "grp", ref_group = "B") %>%
181		#' estimate_odds_ratio(vars = "rsp")
182		#'
183		#' build_table(l, df = dta)
184		#'
185		#' @export
186		#' @order 2
187		estimate_odds_ratio <- function(lyt,
188		vars,
189		variables = list(arm = NULL, strata = NULL),
190		conf_level = 0.95,
191		groups_list = NULL,
192		na_str = default_na_str(),
193		nested = TRUE,
194		...,
195		show_labels = "hidden",
196		table_names = vars,
197		.stats = "or_ci",
198		.formats = NULL,
199		.labels = NULL,
200		.indent_mods = NULL) {
201	4x	extra_args <- list(variables = variables, conf_level = conf_level, groups_list = groups_list, ...)
202
203	4x	afun <- make_afun(
204	4x	a_odds_ratio,
205	4x	.stats = .stats,
206	4x	.formats = .formats,
207	4x	.labels = .labels,
208	4x	.indent_mods = .indent_mods
209		)
210
211	4x	analyze(
212	4x	lyt,
213	4x	vars,
214	4x	afun = afun,
215	4x	na_str = na_str,
216	4x	nested = nested,
217	4x	extra_args = extra_args,
218	4x	show_labels = show_labels,
219	4x	table_names = table_names
220		)
221		}
222
223		#' Helper Functions for Odds Ratio Estimation
224		#'
225		#' @description `r lifecycle::badge("stable")`
226		#'
227		#' Functions to calculate odds ratios in [estimate_odds_ratio()].
228		#'
229		#' @inheritParams argument_convention
230		#' @param data (`data.frame`)\cr data frame containing at least the variables `rsp` and `grp`, and optionally
231		#' `strata` for [or_clogit()].
232		#'
233		#' @return A named `list` of elements `or_ci` and `n_tot`.
234		#'
235		#' @seealso [odds_ratio]
236		#'
237		#' @name h_odds_ratio
238		NULL
239
240		#' @describeIn h_odds_ratio Estimates the odds ratio based on [stats::glm()]. Note that there must be
241		#' exactly 2 groups in `data` as specified by the `grp` variable.
242		#'
243		#' @examples
244		#' # Data with 2 groups.
245		#' data <- data.frame(
246		#' rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)),
247		#' grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)],
248		#' strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)],
249		#' stringsAsFactors = TRUE
250		#' )
251		#'
252		#' # Odds ratio based on glm.
253		#' or_glm(data, conf_level = 0.95)
254		#'
255		#' @export
256		or_glm <- function(data, conf_level) {
257	62x	checkmate::assert_logical(data$rsp)
258	62x	assert_proportion_value(conf_level)
259	62x	assert_df_with_variables(data, list(rsp = "rsp", grp = "grp"))
260	62x	checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))
261
262	62x	data$grp <- as_factor_keep_attributes(data$grp)
263	62x	assert_df_with_factors(data, list(val = "grp"), min.levels = 2, max.levels = 2)
264	62x	formula <- stats::as.formula("rsp ~ grp")
265	62x	model_fit <- stats::glm(
266	62x	formula = formula, data = data,
267	62x	family = stats::binomial(link = "logit")
268		)
269
270		# Note that here we need to discard the intercept.
271	62x	or <- exp(stats::coef(model_fit)[-1])
272	62x	or_ci <- exp(
273	62x	stats::confint.default(model_fit, level = conf_level)[-1, , drop = FALSE]
274		)
275
276	62x	values <- stats::setNames(c(or, or_ci), c("est", "lcl", "ucl"))
277	62x	n_tot <- stats::setNames(nrow(model_fit$model), "n_tot")
278
279	62x	list(or_ci = values, n_tot = n_tot)
280		}
281
282		#' @describeIn h_odds_ratio estimates the odds ratio based on [survival::clogit()]. This is done for
283		#' the whole data set including all groups, since the results are not the same as when doing
284		#' pairwise comparisons between the groups.
285		#'
286		#' @examples
287		#' # Data with 3 groups.
288		#' data <- data.frame(
289		#' rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)),
290		#' grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)],
291		#' strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)],
292		#' stringsAsFactors = TRUE
293		#' )
294		#'
295		#' # Odds ratio based on stratified estimation by conditional logistic regression.
296		#' or_clogit(data, conf_level = 0.95)
297		#'
298		#' @export
299		or_clogit <- function(data, conf_level) {
300	16x	checkmate::assert_logical(data$rsp)
301	16x	assert_proportion_value(conf_level)
302	16x	assert_df_with_variables(data, list(rsp = "rsp", grp = "grp", strata = "strata"))
303	16x	checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))
304	16x	checkmate::assert_multi_class(data$strata, classes = c("factor", "character"))
305
306	16x	data$grp <- as_factor_keep_attributes(data$grp)
307	16x	data$strata <- as_factor_keep_attributes(data$strata)
308
309		# Deviation from convention: `survival::strata` must be simply `strata`.
310	16x	formula <- stats::as.formula("rsp ~ grp + strata(strata)")
311	16x	model_fit <- clogit_with_tryCatch(formula = formula, data = data)
312
313		# Create a list with one set of OR estimates and CI per coefficient, i.e.
314		# comparison of one group vs. the reference group.
315	16x	coef_est <- stats::coef(model_fit)
316	16x	ci_est <- stats::confint(model_fit, level = conf_level)
317	16x	or_ci <- list()
318	16x	for (coef_name in names(coef_est)) {
319	18x	grp_name <- gsub("^grp", "", x = coef_name)
320	18x	or_ci[[grp_name]] <- stats::setNames(
321	18x	object = exp(c(coef_est[coef_name], ci_est[coef_name, , drop = TRUE])),
322	18x	nm = c("est", "lcl", "ucl")
323		)
324		}
325	16x	list(or_ci = or_ci, n_tot = c(n_tot = model_fit$n))
326		}

1		#' Cox Regression Helper: Interactions
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Test and estimate the effect of a treatment in interaction with a covariate.
6		#' The effect is estimated as the HR of the tested treatment for a given level
7		#' of the covariate, in comparison to the treatment control.
8		#'
9		#' @inheritParams argument_convention
10		#' @param x (`numeric` or `factor`)\cr the values of the covariate to be tested.
11		#' @param effect (`string`)\cr the name of the effect to be tested and estimated.
12		#' @param covar (`string`)\cr the name of the covariate in the model.
13		#' @param mod (`coxph`)\cr the Cox regression model.
14		#' @param label (`string`)\cr the label to be returned as `term_label`.
15		#' @param control (`list`)\cr a list of controls as returned by [control_coxreg()].
16		#' @param ... see methods.
17		#'
18		#' @examples
19		#' library(survival)
20		#'
21		#' set.seed(1, kind = "Mersenne-Twister")
22		#'
23		#' # Testing dataset [survival::bladder].
24		#' dta_bladder <- with(
25		#' data = bladder[bladder$enum < 5, ],
26		#' data.frame(
27		#' time = stop,
28		#' status = event,
29		#' armcd = as.factor(rx),
30		#' covar1 = as.factor(enum),
31		#' covar2 = factor(
32		#' sample(as.factor(enum)),
33		#' levels = 1:4,
34		#' labels = c("F", "F", "M", "M")
35		#' )
36		#' )
37		#' )
38		#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
39		#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
40		#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
41		#'
42		#' plot(
43		#' survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
44		#' lty = 2:4,
45		#' xlab = "Months",
46		#' col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
47		#' )
48		#'
49		#' @name cox_regression_inter
50		NULL
51
52		#' @describeIn cox_regression_inter S3 generic helper function to determine interaction effect.
53		#'
54		#' @return
55		#' * `h_coxreg_inter_effect()` returns a `data.frame` of covariate interaction effects consisting of the following
56		#' variables: `effect`, `term`, `term_label`, `level`, `n`, `hr`, `lcl`, `ucl`, `pval`, and `pval_inter`.
57		#'
58		#' @export
59		h_coxreg_inter_effect <- function(x,
60		effect,
61		covar,
62		mod,
63		label,
64		control,
65		...) {
66	26x	UseMethod("h_coxreg_inter_effect", x)
67		}
68
69		#' @describeIn cox_regression_inter Method for `numeric` class. Estimates the interaction with a `numeric` covariate.
70		#'
71		#' @method h_coxreg_inter_effect numeric
72		#'
73		#' @param at (`list`)\cr a list with items named after the covariate, every
74		#' item is a vector of levels at which the interaction should be estimated.
75		#'
76		#' @export
77		h_coxreg_inter_effect.numeric <- function(x,
78		effect,
79		covar,
80		mod,
81		label,
82		control,
83		at,
84		...) {
85	7x	betas <- stats::coef(mod)
86	7x	attrs <- attr(stats::terms(mod), "term.labels")
87	7x	term_indices <- grep(
88	7x	pattern = effect,
89	7x	x = attrs[!grepl("strata\\(", attrs)]
90		)
91	7x	checkmate::assert_vector(term_indices, len = 2)
92	7x	betas <- betas[term_indices]
93	7x	betas_var <- diag(stats::vcov(mod))[term_indices]
94	7x	betas_cov <- stats::vcov(mod)[term_indices[1], term_indices[2]]
95	7x	xval <- if (is.null(at[[covar]])) {
96	6x	stats::median(x)
97		} else {
98	1x	at[[covar]]
99		}
100	7x	effect_index <- !grepl(covar, names(betas))
101	7x	coef_hat <- betas[effect_index] + xval * betas[!effect_index]
102	7x	coef_se <- sqrt(
103	7x	betas_var[effect_index] +
104	7x	xval ^ 2 * betas_var[!effect_index] + # styler: off
105	7x	2 * xval * betas_cov
106		)
107	7x	q_norm <- stats::qnorm((1 + control$conf_level) / 2)
108	7x	data.frame(
109	7x	effect = "Covariate:",
110	7x	term = rep(covar, length(xval)),
111	7x	term_label = paste0(" ", xval),
112	7x	level = as.character(xval),
113	7x	n = NA,
114	7x	hr = exp(coef_hat),
115	7x	lcl = exp(coef_hat - q_norm * coef_se),
116	7x	ucl = exp(coef_hat + q_norm * coef_se),
117	7x	pval = NA,
118	7x	pval_inter = NA,
119	7x	stringsAsFactors = FALSE
120		)
121		}
122
123		#' @describeIn cox_regression_inter Method for `factor` class. Estimate the interaction with a `factor` covariate.
124		#'
125		#' @method h_coxreg_inter_effect factor
126		#'
127		#' @param data (`data.frame`)\cr the data frame on which the model was fit.
128		#'
129		#' @export
130		h_coxreg_inter_effect.factor <- function(x,
131		effect,
132		covar,
133		mod,
134		label,
135		control,
136		data,
137		...) {
138	15x	lvl_given <- levels(x)
139	15x	y <- h_coxreg_inter_estimations(
140	15x	variable = effect, given = covar,
141	15x	lvl_var = levels(data[[effect]]),
142	15x	lvl_given = lvl_given,
143	15x	mod = mod,
144	15x	conf_level = 0.95
145	15x	)[[1]]
146
147	15x	data.frame(
148	15x	effect = "Covariate:",
149	15x	term = rep(covar, nrow(y)),
150	15x	term_label = paste0(" ", lvl_given),
151	15x	level = lvl_given,
152	15x	n = NA,
153	15x	hr = y[, "hr"],
154	15x	lcl = y[, "lcl"],
155	15x	ucl = y[, "ucl"],
156	15x	pval = NA,
157	15x	pval_inter = NA,
158	15x	stringsAsFactors = FALSE
159		)
160		}
161
162		#' @describeIn cox_regression_inter Method for `character` class. Estimate the interaction with a `character` covariate.
163		#' This makes an automatic conversion to `factor` and then forwards to the method for factors.
164		#'
165		#' @method h_coxreg_inter_effect character
166		#'
167		#' @note
168		#' * Automatic conversion of character to factor does not guarantee results can be generated correctly. It is
169		#' therefore better to always pre-process the dataset such that factors are manually created from character
170		#' variables before passing the dataset to [rtables::build_table()].
171		#'
172		#' @export
173		h_coxreg_inter_effect.character <- function(x,
174		effect,
175		covar,
176		mod,
177		label,
178		control,
179		data,
180		...) {
181	4x	y <- as.factor(x)
182
183	4x	h_coxreg_inter_effect(
184	4x	x = y,
185	4x	effect = effect,
186	4x	covar = covar,
187	4x	mod = mod,
188	4x	label = label,
189	4x	control = control,
190	4x	data = data,
191		...
192		)
193		}
194
195		#' @describeIn cox_regression_inter A higher level function to get
196		#' the results of the interaction test and the estimated values.
197		#'
198		#' @return
199		#' * `h_coxreg_extract_interaction()` returns the result of an interaction test and the estimated values. If
200		#' no interaction, [h_coxreg_univar_extract()] is applied instead.
201		#'
202		#' @examples
203		#' mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder)
204		#' h_coxreg_extract_interaction(
205		#' mod = mod, effect = "armcd", covar = "covar1", data = dta_bladder,
206		#' control = control_coxreg()
207		#' )
208		#'
209		#' @export
210		h_coxreg_extract_interaction <- function(effect,
211		covar,
212		mod,
213		data,
214		at,
215		control) {
216	27x	if (!any(attr(stats::terms(mod), "order") == 2)) {
217	10x	y <- h_coxreg_univar_extract(
218	10x	effect = effect, covar = covar, mod = mod, data = data, control = control
219		)
220	10x	y$pval_inter <- NA
221	10x	y
222		} else {
223	17x	test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
224
225		# Test the main treatment effect.
226	17x	mod_aov <- muffled_car_anova(mod, test_statistic)
227	17x	sum_anova <- broom::tidy(mod_aov)
228	17x	pval <- sum_anova[sum_anova$term == effect, ][["p.value"]]
229
230		# Test the interaction effect.
231	17x	pval_inter <- sum_anova[grep(":", sum_anova$term), ][["p.value"]]
232	17x	covar_test <- data.frame(
233	17x	effect = "Covariate:",
234	17x	term = covar,
235	17x	term_label = unname(labels_or_names(data[covar])),
236	17x	level = "",
237	17x	n = mod$n, hr = NA, lcl = NA, ucl = NA, pval = pval,
238	17x	pval_inter = pval_inter,
239	17x	stringsAsFactors = FALSE
240		)
241		# Estimate the interaction.
242	17x	y <- h_coxreg_inter_effect(
243	17x	data[[covar]],
244	17x	covar = covar,
245	17x	effect = effect,
246	17x	mod = mod,
247	17x	label = unname(labels_or_names(data[covar])),
248	17x	at = at,
249	17x	control = control,
250	17x	data = data
251		)
252	17x	rbind(covar_test, y)
253		}
254		}
255
256		#' @describeIn cox_regression_inter Hazard ratio estimation in interactions.
257		#'
258		#' @param variable,given (`string`)\cr the name of variables in interaction. We seek the estimation
259		#' of the levels of `variable` given the levels of `given`.
260		#' @param lvl_var,lvl_given (`character`)\cr corresponding levels has given by [levels()].
261		#' @param mod (`coxph`)\cr a fitted Cox regression model (see [survival::coxph()]).
262		#'
263		#' @details Given the cox regression investigating the effect of Arm (A, B, C; reference A)
264		#' and Sex (F, M; reference Female) and the model being abbreviated: y ~ Arm + Sex + Arm:Sex.
265		#' The cox regression estimates the coefficients along with a variance-covariance matrix for:
266		#'
267		#' - b1 (arm b), b2 (arm c)
268		#' - b3 (sex m)
269		#' - b4 (arm b: sex m), b5 (arm c: sex m)
270		#'
271		#' The estimation of the Hazard Ratio for arm C/sex M is given in reference
272		#' to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5).
273		#' The interaction coefficient is deduced by b2 + b5 while the standard error
274		#' is obtained as $sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$.
275		#'
276		#' @return
277		#' * `h_coxreg_inter_estimations()` returns a list of matrices (one per level of variable) with rows corresponding
278		#' to the combinations of `variable` and `given`, with columns:
279		#' * `coef_hat`: Estimation of the coefficient.
280		#' * `coef_se`: Standard error of the estimation.
281		#' * `hr`: Hazard ratio.
282		#' * `lcl, ucl`: Lower/upper confidence limit of the hazard ratio.
283		#'
284		#' @examples
285		#' mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder)
286		#' result <- h_coxreg_inter_estimations(
287		#' variable = "armcd", given = "covar1",
288		#' lvl_var = levels(dta_bladder$armcd),
289		#' lvl_given = levels(dta_bladder$covar1),
290		#' mod = mod, conf_level = .95
291		#' )
292		#' result
293		#'
294		#' @export
295		h_coxreg_inter_estimations <- function(variable,
296		given,
297		lvl_var,
298		lvl_given,
299		mod,
300		conf_level = 0.95) {
301	16x	var_lvl <- paste0(variable, lvl_var[-1]) # [-1]: reference level
302	16x	giv_lvl <- paste0(given, lvl_given)
303	16x	design_mat <- expand.grid(variable = var_lvl, given = giv_lvl)
304	16x	design_mat <- design_mat[order(design_mat$variable, design_mat$given), ]
305	16x	design_mat <- within(
306	16x	data = design_mat,
307	16x	expr = {
308	16x	inter <- paste0(variable, ":", given)
309	16x	rev_inter <- paste0(given, ":", variable)
310		}
311		)
312	16x	split_by_variable <- design_mat$variable
313	16x	interaction_names <- paste(design_mat$variable, design_mat$given, sep = "/")
314
315	16x	mmat <- stats::model.matrix(mod)[1, ]
316	16x	mmat[!mmat == 0] <- 0
317
318	16x	design_mat <- apply(
319	16x	X = design_mat, MARGIN = 1, FUN = function(x) {
320	46x	mmat[names(mmat) %in% x[-which(names(x) == "given")]] <- 1
321	46x	mmat
322		}
323		)
324	16x	colnames(design_mat) <- interaction_names
325
326	16x	coef <- stats::coef(mod)
327	16x	vcov <- stats::vcov(mod)
328	16x	betas <- as.matrix(coef)
329	16x	coef_hat <- t(design_mat) %*% betas
330	16x	dimnames(coef_hat)[2] <- "coef"
331	16x	coef_se <- apply(
332	16x	design_mat, 2,
333	16x	function(x) {
334	46x	vcov_el <- as.logical(x)
335	46x	y <- vcov[vcov_el, vcov_el]
336	46x	y <- sum(y)
337	46x	y <- sqrt(y)
338	46x	return(y)
339		}
340		)
341	16x	q_norm <- stats::qnorm((1 + conf_level) / 2)
342	16x	y <- cbind(coef_hat, `se(coef)` = coef_se)
343	16x	y <- apply(y, 1, function(x) {
344	46x	x["hr"] <- exp(x["coef"])
345	46x	x["lcl"] <- exp(x["coef"] - q_norm * x["se(coef)"])
346	46x	x["ucl"] <- exp(x["coef"] + q_norm * x["se(coef)"])
347	46x	x
348		})
349	16x	y <- t(y)
350	16x	y <- by(y, split_by_variable, identity)
351	16x	y <- lapply(y, as.matrix)
352	16x	attr(y, "details") <- paste0(
353	16x	"Estimations of ", variable,
354	16x	" hazard ratio given the level of ", given, " compared to ",
355	16x	variable, " level ", lvl_var[1], "."
356		)
357	16x	y
358		}

1		#' Cox Proportional Hazards Regression
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Fits a Cox regression model and estimates hazard ratio to describe the effect size in a survival analysis.
6		#'
7		#' @inheritParams argument_convention
8		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("summarize_coxreg")`
9		#' to see available statistics for this function.
10		#'
11		#' @details Cox models are the most commonly used methods to estimate the magnitude of
12		#' the effect in survival analysis. It assumes proportional hazards: the ratio
13		#' of the hazards between groups (e.g., two arms) is constant over time.
14		#' This ratio is referred to as the "hazard ratio" (HR) and is one of the
15		#' most commonly reported metrics to describe the effect size in survival
16		#' analysis (NEST Team, 2020).
17		#'
18		#' @seealso [fit_coxreg] for relevant fitting functions, [h_cox_regression] for relevant
19		#' helper functions, and [tidy_coxreg] for custom tidy methods.
20		#'
21		#' @examples
22		#' library(survival)
23		#'
24		#' # Testing dataset [survival::bladder].
25		#' set.seed(1, kind = "Mersenne-Twister")
26		#' dta_bladder <- with(
27		#' data = bladder[bladder$enum < 5, ],
28		#' tibble::tibble(
29		#' TIME = stop,
30		#' STATUS = event,
31		#' ARM = as.factor(rx),
32		#' COVAR1 = as.factor(enum) %>% formatters::with_label("A Covariate Label"),
33		#' COVAR2 = factor(
34		#' sample(as.factor(enum)),
35		#' levels = 1:4, labels = c("F", "F", "M", "M")
36		#' ) %>% formatters::with_label("Sex (F/M)")
37		#' )
38		#' )
39		#' dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
40		#' dta_bladder$STUDYID <- factor("X")
41		#'
42		#' u1_variables <- list(
43		#' time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
44		#' )
45		#'
46		#' u2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
47		#'
48		#' m1_variables <- list(
49		#' time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
50		#' )
51		#'
52		#' m2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
53		#'
54		#' @name cox_regression
55		#' @order 1
56		NULL
57
58		#' @describeIn cox_regression Statistics function that transforms results tabulated
59		#' from [fit_coxreg_univar()] or [fit_coxreg_multivar()] into a list.
60		#'
61		#' @param model_df (`data.frame`)\cr contains the resulting model fit from a [fit_coxreg]
62		#' function with tidying applied via [broom::tidy()].
63		#' @param .stats (`character`)\cr the name of statistics to be reported among:
64		#' * `n`: number of observations (univariate only)
65		#' * `hr`: hazard ratio
66		#' * `ci`: confidence interval
67		#' * `pval`: p-value of the treatment effect
68		#' * `pval_inter`: p-value of the interaction effect between the treatment and the covariate (univariate only)
69		#' @param .which_vars (`character`)\cr which rows should statistics be returned for from the given model.
70		#' Defaults to "all". Other options include "var_main" for main effects, `"inter"` for interaction effects,
71		#' and `"multi_lvl"` for multivariate model covariate level rows. When `.which_vars` is "all" specific
72		#' variables can be selected by specifying `.var_nms`.
73		#' @param .var_nms (`character`)\cr the `term` value of rows in `df` for which `.stats` should be returned. Typically
74		#' this is the name of a variable. If using variable labels, `var` should be a vector of both the desired
75		#' variable name and the variable label in that order to see all `.stats` related to that variable. When `.which_vars`
76		#' is `"var_main"` `.var_nms` should be only the variable name.
77		#'
78		#' @return
79		#' * `s_coxreg()` returns the selected statistic for from the Cox regression model for the selected variable(s).
80		#'
81		#' @examples
82		#' # s_coxreg
83		#'
84		#' # Univariate
85		#' univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder)
86		#' df1 <- broom::tidy(univar_model)
87		#'
88		#' s_coxreg(model_df = df1, .stats = "hr")
89		#'
90		#' # Univariate with interactions
91		#' univar_model_inter <- fit_coxreg_univar(
92		#' variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder
93		#' )
94		#' df1_inter <- broom::tidy(univar_model_inter)
95		#'
96		#' s_coxreg(model_df = df1_inter, .stats = "hr", .which_vars = "inter", .var_nms = "COVAR1")
97		#'
98		#' # Univariate without treatment arm - only "COVAR2" covariate effects
99		#' univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder)
100		#' df1_covs <- broom::tidy(univar_covs_model)
101		#'
102		#' s_coxreg(model_df = df1_covs, .stats = "hr", .var_nms = c("COVAR2", "Sex (F/M)"))
103		#'
104		#' # Multivariate.
105		#' multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder)
106		#' df2 <- broom::tidy(multivar_model)
107		#'
108		#' s_coxreg(model_df = df2, .stats = "pval", .which_vars = "var_main", .var_nms = "COVAR1")
109		#' s_coxreg(
110		#' model_df = df2, .stats = "pval", .which_vars = "multi_lvl",
111		#' .var_nms = c("COVAR1", "A Covariate Label")
112		#' )
113		#'
114		#' # Multivariate without treatment arm - only "COVAR1" main effect
115		#' multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder)
116		#' df2_covs <- broom::tidy(multivar_covs_model)
117		#'
118		#' s_coxreg(model_df = df2_covs, .stats = "hr")
119		#'
120		#' @export
121		s_coxreg <- function(model_df, .stats, .which_vars = "all", .var_nms = NULL) {
122	194x	assert_df_with_variables(model_df, list(term = "term", stat = .stats))
123	194x	checkmate::assert_multi_class(model_df$term, classes = c("factor", "character"))
124	194x	model_df$term <- as.character(model_df$term)
125	194x	.var_nms <- .var_nms[!is.na(.var_nms)]
126
127	192x	if (length(.var_nms) > 0) model_df <- model_df[model_df$term %in% .var_nms, ]
128	39x	if (.which_vars == "multi_lvl") model_df$term <- tail(.var_nms, 1)
129
130		# We need a list with names corresponding to the stats to display of equal length to the list of stats.
131	194x	y <- split(model_df, f = model_df$term, drop = FALSE)
132	194x	y <- stats::setNames(y, nm = rep(.stats, length(y)))
133
134	194x	if (.which_vars == "var_main") {
135	84x	y <- lapply(y, function(x) x[1, ]) # only main effect
136	110x	} else if (.which_vars %in% c("inter", "multi_lvl")) {
137	80x	y <- lapply(y, function(x) if (nrow(y[[1]]) > 1) x[-1, ] else x) # exclude main effect
138		}
139
140	194x	lapply(
141	194x	X = y,
142	194x	FUN = function(x) {
143	198x	z <- as.list(x[[.stats]])
144	198x	stats::setNames(z, nm = x$term_label)
145		}
146		)
147		}
148
149		#' @describeIn cox_regression Analysis function which is used as `afun` in [rtables::analyze()]
150		#' and `cfun` in [rtables::summarize_row_groups()] within `summarize_coxreg()`.
151		#'
152		#' @param eff (`flag`)\cr whether treatment effect should be calculated. Defaults to `FALSE`.
153		#' @param var_main (`flag`)\cr whether main effects should be calculated. Defaults to `FALSE`.
154		#' @param na_str (`string`)\cr custom string to replace all `NA` values with. Defaults to `""`.
155		#' @param cache_env (`environment`)\cr an environment object used to cache the regression model in order to
156		#' avoid repeatedly fitting the same model for every row in the table. Defaults to `NULL` (no caching).
157		#' @param varlabels (`list`)\cr a named list corresponds to the names of variables found in data, passed
158		#' as a named list and corresponding to time, event, arm, strata, and covariates terms. If arm is missing
159		#' from variables, then only Cox model(s) including the covariates will be fitted and the corresponding
160		#' effect estimates will be tabulated later.
161		#'
162		#' @return
163		#' * `a_coxreg()` returns formatted [rtables::CellValue()].
164		#'
165		#' @examples
166		#' a_coxreg(
167		#' df = dta_bladder,
168		#' labelstr = "Label 1",
169		#' variables = u1_variables,
170		#' .spl_context = list(value = "COVAR1"),
171		#' .stats = "n",
172		#' .formats = "xx"
173		#' )
174		#'
175		#' a_coxreg(
176		#' df = dta_bladder,
177		#' labelstr = "",
178		#' variables = u1_variables,
179		#' .spl_context = list(value = "COVAR2"),
180		#' .stats = "pval",
181		#' .formats = "xx.xxxx"
182		#' )
183		#'
184		#' @export
185		a_coxreg <- function(df,
186		labelstr,
187		eff = FALSE,
188		var_main = FALSE,
189		multivar = FALSE,
190		variables,
191		at = list(),
192		control = control_coxreg(),
193		.spl_context,
194		.stats,
195		.formats,
196		.indent_mods = NULL,
197		na_str = "",
198		cache_env = NULL) {
199	191x	cov_no_arm <- !multivar && !"arm" %in% names(variables) && control$interaction # special case: univar no arm
200	191x	cov <- tail(.spl_context$value, 1) # current variable/covariate
201	191x	var_lbl <- formatters::var_labels(df)[cov] # check for df labels
202	191x	if (length(labelstr) > 1) {
203	!	labelstr <- if (cov %in% names(labelstr)) labelstr[[cov]] else var_lbl # use df labels if none
204	191x	} else if (!is.na(var_lbl) && labelstr == cov && cov %in% variables$covariates) {
205	62x	labelstr <- var_lbl
206		}
207	191x	if (eff \|\| multivar \|\| cov_no_arm) {
208	82x	control$interaction <- FALSE
209		} else {
210	109x	variables$covariates <- cov
211	40x	if (var_main) control$interaction <- TRUE
212		}
213
214	191x	if (is.null(cache_env[[cov]])) {
215	30x	if (!multivar) {
216	23x	model <- fit_coxreg_univar(variables = variables, data = df, at = at, control = control) %>% broom::tidy()
217		} else {
218	7x	model <- fit_coxreg_multivar(variables = variables, data = df, control = control) %>% broom::tidy()
219		}
220	30x	cache_env[[cov]] <- model
221		} else {
222	161x	model <- cache_env[[cov]]
223		}
224	109x	if (!multivar && !var_main) model[, "pval_inter"] <- NA_real_
225
226	191x	if (cov_no_arm \|\| (!cov_no_arm && !"arm" %in% names(variables) && is.numeric(df[[cov]]))) {
227	15x	multivar <- TRUE
228	3x	if (!cov_no_arm) var_main <- TRUE
229		}
230
231	191x	vars_coxreg <- list(which_vars = "all", var_nms = NULL)
232	191x	if (eff) {
233	40x	if (multivar && !var_main) { # multivar treatment level
234	6x	var_lbl_arm <- formatters::var_labels(df)[[variables$arm]]
235	6x	vars_coxreg[c("var_nms", "which_vars")] <- list(c(variables$arm, var_lbl_arm), "multi_lvl")
236		} else { # treatment effect
237	34x	vars_coxreg["var_nms"] <- variables$arm
238	6x	if (var_main) vars_coxreg["which_vars"] <- "var_main"
239		}
240		} else {
241	151x	if (!multivar \|\| (multivar && var_main && !is.numeric(df[[cov]]))) { # covariate effect/level
242	118x	vars_coxreg[c("var_nms", "which_vars")] <- list(cov, "var_main")
243	33x	} else if (multivar) { # multivar covariate level
244	33x	vars_coxreg[c("var_nms", "which_vars")] <- list(c(cov, var_lbl), "multi_lvl")
245	6x	if (var_main) model[cov, .stats] <- NA_real_
246		}
247	40x	if (!multivar && !var_main && control$interaction) vars_coxreg["which_vars"] <- "inter" # interaction effect
248		}
249	191x	var_vals <- s_coxreg(model, .stats, .which_vars = vars_coxreg$which_vars, .var_nms = vars_coxreg$var_nms)[[1]]
250	191x	var_names <- if (all(grepl("\\(reference = ", names(var_vals))) && labelstr != tail(.spl_context$value, 1)) {
251	21x	paste(c(labelstr, tail(strsplit(names(var_vals), " ")[[1]], 3)), collapse = " ") # "reference" main effect labels
252	191x	} else if ((!multivar && !eff && !(!var_main && control$interaction) && nchar(labelstr) > 0) \|\|
253	191x	(multivar && var_main && is.numeric(df[[cov]]))) { # nolint
254	47x	labelstr # other main effect labels
255	191x	} else if (multivar && !eff && !var_main && is.numeric(df[[cov]])) {
256	6x	"All" # multivar numeric covariate
257		} else {
258	117x	names(var_vals)
259		}
260	191x	in_rows(
261	191x	.list = var_vals, .names = var_names, .labels = var_names, .indent_mods = .indent_mods,
262	191x	.formats = stats::setNames(rep(.formats, length(var_names)), var_names),
263	191x	.format_na_strs = stats::setNames(rep(na_str, length(var_names)), var_names)
264		)
265		}
266
267		#' @describeIn cox_regression Layout-creating function which creates a Cox regression summary table
268		#' layout. This function is a wrapper for several `rtables` layouting functions. This function
269		#' is a wrapper for [rtables::analyze_colvars()] and [rtables::summarize_row_groups()].
270		#'
271		#' @inheritParams fit_coxreg_univar
272		#' @param multivar (`flag`)\cr Defaults to `FALSE`. If `TRUE` multivariate Cox regression will run, otherwise
273		#' univariate Cox regression will run.
274		#' @param common_var (`character`)\cr the name of a factor variable in the dataset which takes the same value
275		#' for all rows. This should be created during pre-processing if no such variable currently exists.
276		#' @param .section_div (`character`)\cr string which should be repeated as a section divider between sections.
277		#' Defaults to `NA` for no section divider. If a vector of two strings are given, the first will be used between
278		#' treatment and covariate sections and the second between different covariates.
279		#'
280		#' @return
281		#' * `summarize_coxreg()` returns a layout object suitable for passing to further layouting functions,
282		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add a Cox regression table
283		#' containing the chosen statistics to the table layout.
284		#'
285		#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()] which also take the `variables`, `data`,
286		#' `at` (univariate only), and `control` arguments but return unformatted univariate and multivariate
287		#' Cox regression models, respectively.
288		#'
289		#' @examples
290		#' # summarize_coxreg
291		#'
292		#' result_univar <- basic_table() %>%
293		#' summarize_coxreg(variables = u1_variables) %>%
294		#' build_table(dta_bladder)
295		#' result_univar
296		#'
297		#' result_univar_covs <- basic_table() %>%
298		#' summarize_coxreg(
299		#' variables = u2_variables,
300		#' ) %>%
301		#' build_table(dta_bladder)
302		#' result_univar_covs
303		#'
304		#' result_multivar <- basic_table() %>%
305		#' summarize_coxreg(
306		#' variables = m1_variables,
307		#' multivar = TRUE,
308		#' ) %>%
309		#' build_table(dta_bladder)
310		#' result_multivar
311		#'
312		#' result_multivar_covs <- basic_table() %>%
313		#' summarize_coxreg(
314		#' variables = m2_variables,
315		#' multivar = TRUE,
316		#' varlabels = c("Covariate 1", "Covariate 2") # custom labels
317		#' ) %>%
318		#' build_table(dta_bladder)
319		#' result_multivar_covs
320		#'
321		#' @export
322		#' @order 2
323		summarize_coxreg <- function(lyt,
324		variables,
325		control = control_coxreg(),
326		at = list(),
327		multivar = FALSE,
328		common_var = "STUDYID",
329		.stats = c("n", "hr", "ci", "pval", "pval_inter"),
330		.formats = c(
331		n = "xx", hr = "xx.xx", ci = "(xx.xx, xx.xx)",
332		pval = "x.xxxx \| (<0.0001)", pval_inter = "x.xxxx \| (<0.0001)"
333		),
334		varlabels = NULL,
335		.indent_mods = NULL,
336		na_str = "",
337		.section_div = NA_character_) {
338	11x	if (multivar && control$interaction) {
339	1x	warning(paste(
340	1x	"Interactions are not available for multivariate cox regression using summarize_coxreg.",
341	1x	"The model will be calculated without interaction effects."
342		))
343		}
344	11x	if (control$interaction && !"arm" %in% names(variables)) {
345	1x	stop("To include interactions please specify 'arm' in variables.")
346		}
347
348	10x	.stats <- if (!"arm" %in% names(variables) \|\| multivar) { # only valid statistics
349	4x	intersect(c("hr", "ci", "pval"), .stats)
350	10x	} else if (control$interaction) {
351	4x	intersect(c("n", "hr", "ci", "pval", "pval_inter"), .stats)
352		} else {
353	2x	intersect(c("n", "hr", "ci", "pval"), .stats)
354		}
355	10x	stat_labels <- c(
356	10x	n = "n", hr = "Hazard Ratio", ci = paste0(control$conf_level * 100, "% CI"),
357	10x	pval = "p-value", pval_inter = "Interaction p-value"
358		)
359	10x	stat_labels <- stat_labels[names(stat_labels) %in% .stats]
360	10x	.formats <- .formats[names(.formats) %in% .stats]
361	10x	env <- new.env() # create caching environment
362
363	10x	lyt <- lyt %>%
364	10x	split_cols_by_multivar(
365	10x	vars = rep(common_var, length(.stats)),
366	10x	varlabels = stat_labels,
367	10x	extra_args = list(
368	10x	.stats = .stats, .formats = .formats, .indent_mods = .indent_mods, na_str = rep(na_str, length(.stats)),
369	10x	cache_env = replicate(length(.stats), list(env))
370		)
371		)
372
373	10x	if ("arm" %in% names(variables)) { # treatment effect
374	8x	lyt <- lyt %>%
375	8x	split_rows_by(
376	8x	common_var,
377	8x	split_label = "Treatment:",
378	8x	label_pos = "visible",
379	8x	child_labels = "hidden",
380	8x	section_div = head(.section_div, 1)
381		)
382	8x	if (!multivar) {
383	6x	lyt <- lyt %>%
384	6x	analyze_colvars(
385	6x	afun = a_coxreg,
386	6x	na_str = na_str,
387	6x	extra_args = list(
388	6x	variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar,
389	6x	labelstr = ""
390		)
391		)
392		} else { # treatment level effects
393	2x	lyt <- lyt %>%
394	2x	summarize_row_groups(
395	2x	cfun = a_coxreg,
396	2x	na_str = na_str,
397	2x	extra_args = list(
398	2x	variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar
399		)
400		) %>%
401	2x	analyze_colvars(
402	2x	afun = a_coxreg,
403	2x	na_str = na_str,
404	2x	extra_args = list(eff = TRUE, control = control, variables = variables, multivar = multivar, labelstr = "")
405		)
406		}
407		}
408
409	10x	if ("covariates" %in% names(variables)) { # covariate main effects
410	10x	lyt <- lyt %>%
411	10x	split_rows_by_multivar(
412	10x	vars = variables$covariates,
413	10x	varlabels = varlabels,
414	10x	split_label = "Covariate:",
415	10x	nested = FALSE,
416	10x	child_labels = if (multivar \|\| control$interaction \|\| !"arm" %in% names(variables)) "default" else "hidden",
417	10x	section_div = tail(.section_div, 1)
418		)
419	10x	if (multivar \|\| control$interaction \|\| !"arm" %in% names(variables)) {
420	8x	lyt <- lyt %>%
421	8x	summarize_row_groups(
422	8x	cfun = a_coxreg,
423	8x	na_str = na_str,
424	8x	extra_args = list(
425	8x	variables = variables, at = at, control = control, multivar = multivar,
426	8x	var_main = if (multivar) multivar else control$interaction
427		)
428		)
429		} else {
430	!	if (!is.null(varlabels)) names(varlabels) <- variables$covariates
431	2x	lyt <- lyt %>%
432	2x	analyze_colvars(
433	2x	afun = a_coxreg,
434	2x	na_str = na_str,
435	2x	extra_args = list(
436	2x	variables = variables, at = at, control = control, multivar = multivar,
437	2x	var_main = if (multivar) multivar else control$interaction,
438	2x	labelstr = if (is.null(varlabels)) "" else varlabels
439		)
440		)
441		}
442
443	2x	if (!"arm" %in% names(variables)) control$interaction <- TRUE # special case: univar no arm
444	10x	if (multivar \|\| control$interaction) { # covariate level effects
445	8x	lyt <- lyt %>%
446	8x	analyze_colvars(
447	8x	afun = a_coxreg,
448	8x	na_str = na_str,
449	8x	extra_args = list(variables = variables, at = at, control = control, multivar = multivar, labelstr = ""),
450	8x	indent_mod = if (!"arm" %in% names(variables) \|\| multivar) 0L else -1L
451		)
452		}
453		}
454
455	10x	lyt
456		}

1		#' Occurrence Table Pruning
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Family of constructor and condition functions to flexibly prune occurrence tables.
6		#' The condition functions always return whether the row result is higher than the threshold.
7		#' Since they are of class [CombinationFunction()] they can be logically combined with other condition
8		#' functions.
9		#'
10		#' @note Since most table specifications are worded positively, we name our constructor and condition
11		#' functions positively, too. However, note that the result of [keep_rows()] says what
12		#' should be pruned, to conform with the [rtables::prune_table()] interface.
13		#'
14		#' @examples
15		#' \donttest{
16		#' tab <- basic_table() %>%
17		#' split_cols_by("ARM") %>%
18		#' split_rows_by("RACE") %>%
19		#' split_rows_by("STRATA1") %>%
20		#' summarize_row_groups() %>%
21		#' analyze_vars("COUNTRY", .stats = "count_fraction") %>%
22		#' build_table(DM)
23		#' }
24		#'
25		#' @name prune_occurrences
26		NULL
27
28		#' @describeIn prune_occurrences Constructor for creating pruning functions based on
29		#' a row condition function. This removes all analysis rows (`TableRow`) that should be
30		#' pruned, i.e., don't fulfill the row condition. It removes the sub-tree if there are no
31		#' children left.
32		#'
33		#' @param row_condition (`CombinationFunction`)\cr condition function which works on individual
34		#' analysis rows and flags whether these should be kept in the pruned table.
35		#'
36		#' @return
37		#' * `keep_rows()` returns a pruning function that can be used with [rtables::prune_table()]
38		#' to prune an `rtables` table.
39		#'
40		#' @examples
41		#' \donttest{
42		#' # `keep_rows`
43		#' is_non_empty <- !CombinationFunction(all_zero_or_na)
44		#' prune_table(tab, keep_rows(is_non_empty))
45		#' }
46		#'
47		#' @export
48		keep_rows <- function(row_condition) {
49	6x	checkmate::assert_function(row_condition)
50	6x	function(table_tree) {
51	2256x	if (inherits(table_tree, "TableRow")) {
52	1872x	return(!row_condition(table_tree))
53		}
54	384x	children <- tree_children(table_tree)
55	384x	identical(length(children), 0L)
56		}
57		}
58
59		#' @describeIn prune_occurrences Constructor for creating pruning functions based on
60		#' a condition for the (first) content row in leaf tables. This removes all leaf tables where
61		#' the first content row does not fulfill the condition. It does not check individual rows.
62		#' It then proceeds recursively by removing the sub tree if there are no children left.
63		#'
64		#' @param content_row_condition (`CombinationFunction`)\cr condition function which works on individual
65		#' first content rows of leaf tables and flags whether these leaf tables should be kept in the pruned table.
66		#'
67		#' @return
68		#' * `keep_content_rows()` returns a pruning function that checks the condition on the first content
69		#' row of leaf tables in the table.
70		#'
71		#' @examples
72		#' # `keep_content_rows`
73		#' \donttest{
74		#' more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab))
75		#' prune_table(tab, keep_content_rows(more_than_twenty))
76		#' }
77		#'
78		#' @export
79		keep_content_rows <- function(content_row_condition) {
80	1x	checkmate::assert_function(content_row_condition)
81	1x	function(table_tree) {
82	166x	if (is_leaf_table(table_tree)) {
83	24x	content_row <- h_content_first_row(table_tree)
84	24x	return(!content_row_condition(content_row))
85		}
86	142x	if (inherits(table_tree, "DataRow")) {
87	120x	return(FALSE)
88		}
89	22x	children <- tree_children(table_tree)
90	22x	identical(length(children), 0L)
91		}
92		}
93
94		#' @describeIn prune_occurrences Constructor for creating condition functions on total counts in the specified columns.
95		#'
96		#' @param atleast (`count` or `proportion`)\cr threshold which should be met in order to keep the row.
97		#' @param ... arguments for row or column access, see [`rtables_access`]: either `col_names` (`character`) including
98		#' the names of the columns which should be used, or alternatively `col_indices` (`integer`) giving the indices
99		#' directly instead.
100		#'
101		#' @return
102		#' * `has_count_in_cols()` returns a condition function that sums the counts in the specified column.
103		#'
104		#' @examples
105		#' \donttest{
106		#' more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab))
107		#' prune_table(tab, keep_rows(more_than_one))
108		#' }
109		#'
110		#' @export
111		has_count_in_cols <- function(atleast, ...) {
112	3x	checkmate::assert_count(atleast)
113	3x	CombinationFunction(function(table_row) {
114	334x	row_counts <- h_row_counts(table_row, ...)
115	334x	total_count <- sum(row_counts)
116	334x	total_count >= atleast
117		})
118		}
119
120		#' @describeIn prune_occurrences Constructor for creating condition functions on any of the counts in
121		#' the specified columns satisfying a threshold.
122		#'
123		#' @param atleast (`count` or `proportion`)\cr threshold which should be met in order to keep the row.
124		#'
125		#' @return
126		#' * `has_count_in_any_col()` returns a condition function that compares the counts in the
127		#' specified columns with the threshold.
128		#'
129		#' @examples
130		#' \donttest{
131		#' # `has_count_in_any_col`
132		#' any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab))
133		#' prune_table(tab, keep_rows(any_more_than_one))
134		#' }
135		#'
136		#' @export
137		has_count_in_any_col <- function(atleast, ...) {
138	!	checkmate::assert_count(atleast)
139	!	CombinationFunction(function(table_row) {
140	!	row_counts <- h_row_counts(table_row, ...)
141	!	any(row_counts >= atleast)
142		})
143		}
144
145		#' @describeIn prune_occurrences Constructor for creating condition functions on total fraction in
146		#' the specified columns.
147		#'
148		#' @return
149		#' * `has_fraction_in_cols()` returns a condition function that sums the counts in the
150		#' specified column, and computes the fraction by dividing by the total column counts.
151		#'
152		#' @examples
153		#' \donttest{
154		#' # `has_fraction_in_cols`
155		#' more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab))
156		#' prune_table(tab, keep_rows(more_than_five_percent))
157		#' }
158		#'
159		#' @export
160		has_fraction_in_cols <- function(atleast, ...) {
161	1x	assert_proportion_value(atleast, include_boundaries = TRUE)
162	1x	CombinationFunction(function(table_row) {
163	303x	row_counts <- h_row_counts(table_row, ...)
164	303x	total_count <- sum(row_counts)
165	303x	col_counts <- h_col_counts(table_row, ...)
166	303x	total_n <- sum(col_counts)
167	303x	total_percent <- total_count / total_n
168	303x	total_percent >= atleast
169		})
170		}
171
172		#' @describeIn prune_occurrences Constructor for creating condition functions on any fraction in
173		#' the specified columns.
174		#'
175		#' @return
176		#' * `has_fraction_in_any_col()` returns a condition function that looks at the fractions
177		#' in the specified columns and checks whether any of them fulfill the threshold.
178		#'
179		#' @examples
180		#' \donttest{
181		#' # `has_fraction_in_any_col`
182		#' any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab))
183		#' prune_table(tab, keep_rows(more_than_five_percent))
184		#' }
185		#'
186		#' @export
187		has_fraction_in_any_col <- function(atleast, ...) {
188	!	assert_proportion_value(atleast, include_boundaries = TRUE)
189	!	CombinationFunction(function(table_row) {
190	!	row_fractions <- h_row_fractions(table_row, ...)
191	!	any(row_fractions >= atleast)
192		})
193		}
194
195		#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
196		#' between the fractions reported in each specified column.
197		#'
198		#' @return
199		#' * `has_fractions_difference()` returns a condition function that extracts the fractions of each
200		#' specified column, and computes the difference of the minimum and maximum.
201		#'
202		#' @examples
203		#' \donttest{
204		#' # `has_fractions_difference`
205		#' more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab))
206		#' prune_table(tab, keep_rows(more_than_five_percent_diff))
207		#' }
208		#'
209		#' @export
210		has_fractions_difference <- function(atleast, ...) {
211	1x	assert_proportion_value(atleast, include_boundaries = TRUE)
212	1x	CombinationFunction(function(table_row) {
213	243x	fractions <- h_row_fractions(table_row, ...)
214	243x	difference <- diff(range(fractions))
215	243x	difference >= atleast
216		})
217		}
218
219		#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
220		#' between the counts reported in each specified column.
221		#'
222		#' @return
223		#' * `has_counts_difference()` returns a condition function that extracts the counts of each
224		#' specified column, and computes the difference of the minimum and maximum.
225		#'
226		#' @examples
227		#' \donttest{
228		#' more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab))
229		#' prune_table(tab, keep_rows(more_than_one_diff))
230		#' }
231		#'
232		#' @export
233		has_counts_difference <- function(atleast, ...) {
234	1x	checkmate::assert_count(atleast)
235	1x	CombinationFunction(function(table_row) {
236	27x	counts <- h_row_counts(table_row, ...)
237	27x	difference <- diff(range(counts))
238	27x	difference >= atleast
239		})
240		}

1		#' Helper Functions for Tabulating Binary Response by Subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions that tabulate in a data frame statistics such as response rate
6		#' and odds ratio for population subgroups.
7		#'
8		#' @inheritParams argument_convention
9		#' @inheritParams response_subgroups
10		#' @param arm (`factor`)\cr the treatment group variable.
11		#'
12		#' @details Main functionality is to prepare data for use in a layout-creating function.
13		#'
14		#' @examples
15		#' library(dplyr)
16		#' library(forcats)
17		#'
18		#' adrs <- tern_ex_adrs
19		#' adrs_labels <- formatters::var_labels(adrs)
20		#'
21		#' adrs_f <- adrs %>%
22		#' filter(PARAMCD == "BESRSPI") %>%
23		#' filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
24		#' droplevels() %>%
25		#' mutate(
26		#' # Reorder levels of factor to make the placebo group the reference arm.
27		#' ARM = fct_relevel(ARM, "B: Placebo"),
28		#' rsp = AVALC == "CR"
29		#' )
30		#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
31		#'
32		#' @name h_response_subgroups
33		NULL
34
35		#' @describeIn h_response_subgroups helper to prepare a data frame of binary responses by arm.
36		#'
37		#' @return
38		#' * `h_proportion_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, and `prop`.
39		#'
40		#' @examples
41		#' h_proportion_df(
42		#' c(TRUE, FALSE, FALSE),
43		#' arm = factor(c("A", "A", "B"), levels = c("A", "B"))
44		#' )
45		#'
46		#' @export
47		h_proportion_df <- function(rsp, arm) {
48	64x	checkmate::assert_logical(rsp)
49	63x	assert_valid_factor(arm, len = length(rsp))
50	63x	non_missing_rsp <- !is.na(rsp)
51	63x	rsp <- rsp[non_missing_rsp]
52	63x	arm <- arm[non_missing_rsp]
53
54	63x	lst_rsp <- split(rsp, arm)
55	63x	lst_results <- Map(function(x, arm) {
56	126x	if (length(x) > 0) {
57	124x	s_prop <- s_proportion(df = x)
58	124x	data.frame(
59	124x	arm = arm,
60	124x	n = length(x),
61	124x	n_rsp = unname(s_prop$n_prop[1]),
62	124x	prop = unname(s_prop$n_prop[2]),
63	124x	stringsAsFactors = FALSE
64		)
65		} else {
66	2x	data.frame(
67	2x	arm = arm,
68	2x	n = 0L,
69	2x	n_rsp = NA,
70	2x	prop = NA,
71	2x	stringsAsFactors = FALSE
72		)
73		}
74	63x	}, lst_rsp, names(lst_rsp))
75
76	63x	df <- do.call(rbind, args = c(lst_results, make.row.names = FALSE))
77	63x	df$arm <- factor(df$arm, levels = levels(arm))
78	63x	df
79		}
80
81		#' @describeIn h_response_subgroups summarizes proportion of binary responses by arm and across subgroups
82		#' in a data frame. `variables` corresponds to the names of variables found in `data`, passed as a named list and
83		#' requires elements `rsp`, `arm` and optionally `subgroups`. `groups_lists` optionally specifies
84		#' groupings for `subgroups` variables.
85		#'
86		#' @return
87		#' * `h_proportion_subgroups_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`,
88		#' `var`, `var_label`, and `row_type`.
89		#'
90		#' @examples
91		#' h_proportion_subgroups_df(
92		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
93		#' data = adrs_f
94		#' )
95		#'
96		#' # Define groupings for BMRKR2 levels.
97		#' h_proportion_subgroups_df(
98		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
99		#' data = adrs_f,
100		#' groups_lists = list(
101		#' BMRKR2 = list(
102		#' "low" = "LOW",
103		#' "low/medium" = c("LOW", "MEDIUM"),
104		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
105		#' )
106		#' )
107		#' )
108		#'
109		#' @export
110		h_proportion_subgroups_df <- function(variables,
111		data,
112		groups_lists = list(),
113		label_all = "All Patients") {
114	14x	checkmate::assert_character(variables$rsp)
115	14x	checkmate::assert_character(variables$arm)
116	14x	checkmate::assert_character(variables$subgroups, null.ok = TRUE)
117	14x	assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
118	14x	assert_df_with_variables(data, variables)
119	14x	checkmate::assert_string(label_all)
120
121		# Add All Patients.
122	14x	result_all <- h_proportion_df(data[[variables$rsp]], data[[variables$arm]])
123	14x	result_all$subgroup <- label_all
124	14x	result_all$var <- "ALL"
125	14x	result_all$var_label <- label_all
126	14x	result_all$row_type <- "content"
127
128		# Add Subgroups.
129	14x	if (is.null(variables$subgroups)) {
130	3x	result_all
131		} else {
132	11x	l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
133
134	11x	l_result <- lapply(l_data, function(grp) {
135	46x	result <- h_proportion_df(grp$df[[variables$rsp]], grp$df[[variables$arm]])
136	46x	result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
137	46x	cbind(result, result_labels)
138		})
139	11x	result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
140	11x	result_subgroups$row_type <- "analysis"
141
142	11x	rbind(
143	11x	result_all,
144	11x	result_subgroups
145		)
146		}
147		}
148
149		#' @describeIn h_response_subgroups helper to prepare a data frame with estimates of
150		#' the odds ratio between a treatment and a control arm.
151		#'
152		#' @inheritParams response_subgroups
153		#' @param strata_data (`factor`, `data.frame` or `NULL`)\cr required if stratified analysis is performed.
154		#'
155		#' @return
156		#' * `h_odds_ratio_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`, and
157		#' optionally `pval` and `pval_label`.
158		#'
159		#' @examples
160		#' # Unstratatified analysis.
161		#' h_odds_ratio_df(
162		#' c(TRUE, FALSE, FALSE, TRUE),
163		#' arm = factor(c("A", "A", "B", "B"), levels = c("A", "B"))
164		#' )
165		#'
166		#' # Include p-value.
167		#' h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = "chisq")
168		#'
169		#' # Stratatified analysis.
170		#' h_odds_ratio_df(
171		#' rsp = adrs_f$rsp,
172		#' arm = adrs_f$ARM,
173		#' strata_data = adrs_f[, c("STRATA1", "STRATA2")],
174		#' method = "cmh"
175		#' )
176		#'
177		#' @export
178		h_odds_ratio_df <- function(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL) {
179	69x	assert_valid_factor(arm, n.levels = 2, len = length(rsp))
180
181	69x	df_rsp <- data.frame(
182	69x	rsp = rsp,
183	69x	arm = arm
184		)
185
186	69x	if (!is.null(strata_data)) {
187	11x	strata_var <- interaction(strata_data, drop = TRUE)
188	11x	strata_name <- "strata"
189
190	11x	assert_valid_factor(strata_var, len = nrow(df_rsp))
191
192	11x	df_rsp[[strata_name]] <- strata_var
193		} else {
194	58x	strata_name <- NULL
195		}
196
197	69x	l_df <- split(df_rsp, arm)
198
199	69x	if (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) > 0) {
200		# Odds ratio and CI.
201	67x	result_odds_ratio <- s_odds_ratio(
202	67x	df = l_df[[2]],
203	67x	.var = "rsp",
204	67x	.ref_group = l_df[[1]],
205	67x	.in_ref_col = FALSE,
206	67x	.df_row = df_rsp,
207	67x	variables = list(arm = "arm", strata = strata_name),
208	67x	conf_level = conf_level
209		)
210
211	67x	df <- data.frame(
212		# Dummy column needed downstream to create a nested header.
213	67x	arm = " ",
214	67x	n_tot = unname(result_odds_ratio$n_tot["n_tot"]),
215	67x	or = unname(result_odds_ratio$or_ci["est"]),
216	67x	lcl = unname(result_odds_ratio$or_ci["lcl"]),
217	67x	ucl = unname(result_odds_ratio$or_ci["ucl"]),
218	67x	conf_level = conf_level,
219	67x	stringsAsFactors = FALSE
220		)
221
222	67x	if (!is.null(method)) {
223		# Test for difference.
224	34x	result_test <- s_test_proportion_diff(
225	34x	df = l_df[[2]],
226	34x	.var = "rsp",
227	34x	.ref_group = l_df[[1]],
228	34x	.in_ref_col = FALSE,
229	34x	variables = list(strata = strata_name),
230	34x	method = method
231		)
232
233	34x	df$pval <- as.numeric(result_test$pval)
234	34x	df$pval_label <- obj_label(result_test$pval)
235		}
236
237		# In those cases cannot go through the model so will obtain n_tot from data.
238		} else if (
239	2x	(nrow(l_df[[1]]) == 0 && nrow(l_df[[2]]) > 0) \|\|
240	2x	(nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) == 0)
241		) {
242	2x	df <- data.frame(
243		# Dummy column needed downstream to create a nested header.
244	2x	arm = " ",
245	2x	n_tot = sum(stats::complete.cases(df_rsp)),
246	2x	or = NA,
247	2x	lcl = NA,
248	2x	ucl = NA,
249	2x	conf_level = conf_level,
250	2x	stringsAsFactors = FALSE
251		)
252	2x	if (!is.null(method)) {
253	2x	df$pval <- NA
254	2x	df$pval_label <- NA
255		}
256		} else {
257	!	df <- data.frame(
258		# Dummy column needed downstream to create a nested header.
259	!	arm = " ",
260	!	n_tot = 0L,
261	!	or = NA,
262	!	lcl = NA,
263	!	ucl = NA,
264	!	conf_level = conf_level,
265	!	stringsAsFactors = FALSE
266		)
267
268	!	if (!is.null(method)) {
269	!	df$pval <- NA
270	!	df$pval_label <- NA
271		}
272		}
273
274	69x	df
275		}
276
277		#' @describeIn h_response_subgroups summarizes estimates of the odds ratio between a treatment and a control
278		#' arm across subgroups in a data frame. `variables` corresponds to the names of variables found in
279		#' `data`, passed as a named list and requires elements `rsp`, `arm` and optionally `subgroups`
280		#' and `strata`. `groups_lists` optionally specifies groupings for `subgroups` variables.
281		#'
282		#' @return
283		#' * `h_odds_ratio_subgroups_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`,
284		#' `conf_level`, `subgroup`, `var`, `var_label`, and `row_type`.
285		#'
286		#' @examples
287		#' # Unstratified analysis.
288		#' h_odds_ratio_subgroups_df(
289		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
290		#' data = adrs_f
291		#' )
292		#'
293		#' # Stratified analysis.
294		#' h_odds_ratio_subgroups_df(
295		#' variables = list(
296		#' rsp = "rsp",
297		#' arm = "ARM",
298		#' subgroups = c("SEX", "BMRKR2"),
299		#' strata = c("STRATA1", "STRATA2")
300		#' ),
301		#' data = adrs_f
302		#' )
303		#'
304		#' # Define groupings of BMRKR2 levels.
305		#' h_odds_ratio_subgroups_df(
306		#' variables = list(
307		#' rsp = "rsp",
308		#' arm = "ARM",
309		#' subgroups = c("SEX", "BMRKR2")
310		#' ),
311		#' data = adrs_f,
312		#' groups_lists = list(
313		#' BMRKR2 = list(
314		#' "low" = "LOW",
315		#' "low/medium" = c("LOW", "MEDIUM"),
316		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
317		#' )
318		#' )
319		#' )
320		#'
321		#' @export
322		h_odds_ratio_subgroups_df <- function(variables,
323		data,
324		groups_lists = list(),
325		conf_level = 0.95,
326		method = NULL,
327		label_all = "All Patients") {
328	15x	if ("strat" %in% names(variables)) {
329	!	warning(
330	!	"Warning: the `strat` element name of the `variables` list argument to `h_odds_ratio_subgroups_df() ",
331	!	"was deprecated in tern 0.9.3.\n ",
332	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
333		)
334	!	variables[["strata"]] <- variables[["strat"]]
335		}
336
337	15x	checkmate::assert_character(variables$rsp)
338	15x	checkmate::assert_character(variables$arm)
339	15x	checkmate::assert_character(variables$subgroups, null.ok = TRUE)
340	15x	checkmate::assert_character(variables$strata, null.ok = TRUE)
341	15x	assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
342	15x	assert_df_with_variables(data, variables)
343	15x	checkmate::assert_string(label_all)
344
345	15x	strata_data <- if (is.null(variables$strata)) {
346	13x	NULL
347		} else {
348	2x	data[, variables$strata, drop = FALSE]
349		}
350
351		# Add All Patients.
352	15x	result_all <- h_odds_ratio_df(
353	15x	rsp = data[[variables$rsp]],
354	15x	arm = data[[variables$arm]],
355	15x	strata_data = strata_data,
356	15x	conf_level = conf_level,
357	15x	method = method
358		)
359	15x	result_all$subgroup <- label_all
360	15x	result_all$var <- "ALL"
361	15x	result_all$var_label <- label_all
362	15x	result_all$row_type <- "content"
363
364	15x	if (is.null(variables$subgroups)) {
365	3x	result_all
366		} else {
367	12x	l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
368
369	12x	l_result <- lapply(l_data, function(grp) {
370	50x	grp_strata_data <- if (is.null(variables$strata)) {
371	42x	NULL
372		} else {
373	8x	grp$df[, variables$strata, drop = FALSE]
374		}
375
376	50x	result <- h_odds_ratio_df(
377	50x	rsp = grp$df[[variables$rsp]],
378	50x	arm = grp$df[[variables$arm]],
379	50x	strata_data = grp_strata_data,
380	50x	conf_level = conf_level,
381	50x	method = method
382		)
383	50x	result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
384	50x	cbind(result, result_labels)
385		})
386
387	12x	result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
388	12x	result_subgroups$row_type <- "analysis"
389
390	12x	rbind(
391	12x	result_all,
392	12x	result_subgroups
393		)
394		}
395		}

1		# Utility functions to cooperate with {rtables} package
2
3		#' Convert Table into Matrix of Strings
4		#'
5		#' @description `r lifecycle::badge("stable")`
6		#'
7		#' Helper function to use mostly within tests. `with_spaces`parameter allows
8		#' to test not only for content but also indentation and table structure.
9		#' `print_txt_to_copy` instead facilitate the testing development by returning a well
10		#' formatted text that needs only to be copied and pasted in the expected output.
11		#'
12		#' @inheritParams formatters::toString
13		#' @param x `rtables` table.
14		#' @param with_spaces (`logical`)\cr should the tested table keep the indentation and other relevant spaces?
15		#' @param print_txt_to_copy (`logical`)\cr utility to have a way to copy the input table directly
16		#' into the expected variable instead of copying it too manually.
17		#'
18		#' @return A `matrix` of `string`s. If `print_txt_to_copy = TRUE` the well formatted printout of the
19		#' table will be printed to console, ready to be copied as a expected value.
20		#'
21		#' @examples
22		#' tbl <- basic_table() %>%
23		#' split_rows_by("SEX") %>%
24		#' split_cols_by("ARM") %>%
25		#' analyze("AGE") %>%
26		#' build_table(tern_ex_adsl)
27		#'
28		#' to_string_matrix(tbl, widths = ceiling(propose_column_widths(tbl) / 2))
29		#'
30		#' @export
31		to_string_matrix <- function(x, widths = NULL, max_width = NULL,
32		hsep = formatters::default_hsep(),
33		with_spaces = TRUE, print_txt_to_copy = FALSE) {
34	5x	checkmate::assert_flag(with_spaces)
35	5x	checkmate::assert_flag(print_txt_to_copy)
36	5x	checkmate::assert_int(max_width, null.ok = TRUE)
37
38	5x	if (inherits(x, "MatrixPrintForm")) {
39	!	tx <- x
40		} else {
41	5x	tx <- matrix_form(x, TRUE)
42		}
43
44	5x	tf_wrap <- FALSE
45	5x	if (!is.null(max_width)) {
46	!	tf_wrap <- TRUE
47		}
48
49		# Producing the matrix to test
50	5x	if (with_spaces) {
51	!	out <- strsplit(toString(tx, widths = widths, tf_wrap = tf_wrap, max_width = max_width, hsep = hsep), "\\n")[[1]]
52		} else {
53	5x	out <- tx$strings
54		}
55
56		# Printing to console formatted output that needs to be copied in "expected"
57	5x	if (print_txt_to_copy) {
58	!	out_tmp <- out
59	!	if (!with_spaces) {
60	!	out_tmp <- apply(out, 1, paste0, collapse = '", "')
61		}
62	!	cat(paste0('c(\n "', paste0(out_tmp, collapse = '",\n "'), '"\n)'))
63		}
64
65		# Return values
66	5x	return(out)
67		}
68
69		#' Blank for Missing Input
70		#'
71		#' Helper function to use in tabulating model results.
72		#'
73		#' @param x (`vector`)\cr input for a cell.
74		#'
75		#' @return An empty `character` vector if all entries in `x` are missing (`NA`), otherwise
76		#' the unlisted version of `x`.
77		#'
78		#' @keywords internal
79		unlist_and_blank_na <- function(x) {
80	267x	unl <- unlist(x)
81	267x	if (all(is.na(unl))) {
82	161x	character()
83		} else {
84	106x	unl
85		}
86		}
87
88		#' Constructor for Content Functions given Data Frame with Flag Input
89		#'
90		#' This can be useful for tabulating model results.
91		#'
92		#' @param analysis_var (`string`)\cr variable name for the column containing values to be returned by the
93		#' content function.
94		#' @param flag_var (`string`)\cr variable name for the logical column identifying which row should be returned.
95		#' @param format (`string`)\cr `rtables` format to use.
96		#'
97		#' @return A content function which gives `df$analysis_var` at the row identified by
98		#' `.df_row$flag` in the given format.
99		#'
100		#' @keywords internal
101		cfun_by_flag <- function(analysis_var,
102		flag_var,
103		format = "xx",
104		.indent_mods = NULL) {
105	61x	checkmate::assert_string(analysis_var)
106	61x	checkmate::assert_string(flag_var)
107	61x	function(df, labelstr) {
108	265x	row_index <- which(df[[flag_var]])
109	265x	x <- unlist_and_blank_na(df[[analysis_var]][row_index])
110	265x	formatters::with_label(
111	265x	rcell(x, format = format, indent_mod = .indent_mods),
112	265x	labelstr
113		)
114		}
115		}
116
117		#' Content Row Function to Add Row Total to Labels
118		#'
119		#' This takes the label of the latest row split level and adds the row total from `df` in parentheses.
120		#' This function differs from [c_label_n_alt()] by taking row counts from `df` rather than
121		#' `alt_counts_df`, and is used by [add_rowcounts()] when `alt_counts` is set to `FALSE`.
122		#'
123		#' @inheritParams argument_convention
124		#'
125		#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.
126		#'
127		#' @note It is important here to not use `df` but rather `.N_row` in the implementation, because
128		#' the former is already split by columns and will refer to the first column of the data only.
129		#'
130		#' @seealso [c_label_n_alt()] which performs the same function but retrieves row counts from
131		#' `alt_counts_df` instead of `df`.
132		#'
133		#' @keywords internal
134		c_label_n <- function(df,
135		labelstr,
136		.N_row) { # nolint
137	270x	label <- paste0(labelstr, " (N=", .N_row, ")")
138	270x	in_rows(
139	270x	.list = list(row_count = formatters::with_label(c(.N_row, .N_row), label)),
140	270x	.formats = c(row_count = function(x, ...) "")
141		)
142		}
143
144		#' Content Row Function to Add `alt_counts_df` Row Total to Labels
145		#'
146		#' This takes the label of the latest row split level and adds the row total from `alt_counts_df`
147		#' in parentheses. This function differs from [c_label_n()] by taking row counts from `alt_counts_df`
148		#' rather than `df`, and is used by [add_rowcounts()] when `alt_counts` is set to `TRUE`.
149		#'
150		#' @inheritParams argument_convention
151		#'
152		#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.
153		#'
154		#' @seealso [c_label_n()] which performs the same function but retrieves row counts from `df` instead
155		#' of `alt_counts_df`.
156		#'
157		#' @keywords internal
158		c_label_n_alt <- function(df,
159		labelstr,
160		.alt_df_row) {
161	7x	N_row_alt <- nrow(.alt_df_row) # nolint
162	7x	label <- paste0(labelstr, " (N=", N_row_alt, ")")
163	7x	in_rows(
164	7x	.list = list(row_count = formatters::with_label(c(N_row_alt, N_row_alt), label)),
165	7x	.formats = c(row_count = function(x, ...) "")
166		)
167		}
168
169		#' Layout Creating Function to Add Row Total Counts
170		#'
171		#' @description `r lifecycle::badge("stable")`
172		#'
173		#' This works analogously to [rtables::add_colcounts()] but on the rows. This function
174		#' is a wrapper for [rtables::summarize_row_groups()].
175		#'
176		#' @inheritParams argument_convention
177		#' @param alt_counts (`flag`)\cr whether row counts should be taken from `alt_counts_df` (`TRUE`)
178		#' or from `df` (`FALSE`). Defaults to `FALSE`.
179		#'
180		#' @return A modified layout where the latest row split labels now have the row-wise
181		#' total counts (i.e. without column-based subsetting) attached in parentheses.
182		#'
183		#' @note Row count values are contained in these row count rows but are not displayed
184		#' so that they are not considered zero rows by default when pruning.
185		#'
186		#' @examples
187		#' basic_table() %>%
188		#' split_cols_by("ARM") %>%
189		#' add_colcounts() %>%
190		#' split_rows_by("RACE", split_fun = drop_split_levels) %>%
191		#' add_rowcounts() %>%
192		#' analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx") %>%
193		#' build_table(DM)
194		#'
195		#' @export
196		add_rowcounts <- function(lyt, alt_counts = FALSE) {
197	6x	summarize_row_groups(
198	6x	lyt,
199	6x	cfun = if (alt_counts) c_label_n_alt else c_label_n
200		)
201		}
202
203		#' Obtain Column Indices
204		#'
205		#' @description `r lifecycle::badge("stable")`
206		#'
207		#' Helper function to extract column indices from a `VTableTree` for a given
208		#' vector of column names.
209		#'
210		#' @param table_tree (`VTableTree`)\cr table to extract the indices from.
211		#' @param col_names (`character`)\cr vector of column names.
212		#'
213		#' @return A vector of column indices.
214		#'
215		#' @export
216		h_col_indices <- function(table_tree, col_names) {
217	1232x	checkmate::assert_class(table_tree, "VTableNodeInfo")
218	1232x	checkmate::assert_subset(col_names, names(attr(col_info(table_tree), "cextra_args")), empty.ok = FALSE)
219	1232x	match(col_names, names(attr(col_info(table_tree), "cextra_args")))
220		}
221
222		#' Labels or Names of List Elements
223		#'
224		#' Internal helper function for working with nested statistic function results which typically
225		#' don't have labels but names that we can use.
226		#'
227		#' @param x a list.
228		#'
229		#' @return A `character` vector with the labels or names for the list elements.
230		#'
231		#' @keywords internal
232		labels_or_names <- function(x) {
233	131x	checkmate::assert_multi_class(x, c("data.frame", "list"))
234	131x	labs <- sapply(x, obj_label)
235	131x	nams <- rlang::names2(x)
236	131x	label_is_null <- sapply(labs, is.null)
237	131x	result <- unlist(ifelse(label_is_null, nams, labs))
238	131x	return(result)
239		}
240
241		#' Convert to `rtable`
242		#'
243		#' @description `r lifecycle::badge("stable")`
244		#'
245		#' This is a new generic function to convert objects to `rtable` tables.
246		#'
247		#' @param x the object which should be converted to an `rtable`.
248		#' @param ... additional arguments for methods.
249		#'
250		#' @return An `rtables` table object. Note that the concrete class will depend on the method used.
251		#'
252		#' @export
253		as.rtable <- function(x, ...) { # nolint
254	3x	UseMethod("as.rtable", x)
255		}
256
257		#' @describeIn as.rtable method for converting `data.frame` that contain numeric columns to `rtable`.
258		#'
259		#' @param format the format which should be used for the columns.
260		#'
261		#' @method as.rtable data.frame
262		#'
263		#' @examples
264		#' x <- data.frame(
265		#' a = 1:10,
266		#' b = rnorm(10)
267		#' )
268		#' as.rtable(x)
269		#'
270		#' @export
271		as.rtable.data.frame <- function(x, format = "xx.xx", ...) {
272	3x	checkmate::assert_numeric(unlist(x))
273	2x	do.call(
274	2x	rtable,
275	2x	c(
276	2x	list(
277	2x	header = labels_or_names(x),
278	2x	format = format
279		),
280	2x	Map(
281	2x	function(row, row_name) {
282	20x	do.call(
283	20x	rrow,
284	20x	c(as.list(unname(row)),
285	20x	row.name = row_name
286		)
287		)
288		},
289	2x	row = as.data.frame(t(x)),
290	2x	row_name = rownames(x)
291		)
292		)
293		)
294		}
295
296		#' Split parameters
297		#'
298		#' @description `r lifecycle::badge("stable")`
299		#'
300		#' It divides the data in the vector `param` into the groups defined by `f` based on specified `values`. It is relevant
301		#' in `rtables` layers so as to distribute parameters `.stats` or' `.formats` into lists with items corresponding to
302		#' specific analysis function.
303		#'
304		#' @param param (`vector`)\cr the parameter to be split.
305		#' @param value (`vector`)\cr the value used to split.
306		#' @param f (`list` of `vectors`)\cr the reference to make the split
307		#'
308		#' @return A named `list` with the same element names as `f`, each containing the elements specified in `.stats`.
309		#'
310		#' @examples
311		#' f <- list(
312		#' surv = c("pt_at_risk", "event_free_rate", "rate_se", "rate_ci"),
313		#' surv_diff = c("rate_diff", "rate_diff_ci", "ztest_pval")
314		#' )
315		#'
316		#' .stats <- c("pt_at_risk", "rate_diff")
317		#' h_split_param(.stats, .stats, f = f)
318		#'
319		#' # $surv
320		#' # [1] "pt_at_risk"
321		#' #
322		#' # $surv_diff
323		#' # [1] "rate_diff"
324		#'
325		#' .formats <- c("pt_at_risk" = "xx", "event_free_rate" = "xxx")
326		#' h_split_param(.formats, names(.formats), f = f)
327		#'
328		#' # $surv
329		#' # pt_at_risk event_free_rate
330		#' # "xx" "xxx"
331		#' #
332		#' # $surv_diff
333		#' # NULL
334		#'
335		#' @export
336		h_split_param <- function(param,
337		value,
338		f) {
339	25x	y <- lapply(f, function(x) param[value %in% x])
340	25x	lapply(y, function(x) if (length(x) == 0) NULL else x)
341		}
342
343		#' Get Selected Statistics Names
344		#'
345		#' Helper function to be used for creating `afun`.
346		#'
347		#' @param .stats (`vector` or `NULL`)\cr input to the layout creating function. Note that `NULL` means
348		#' in this context that all default statistics should be used.
349		#' @param all_stats (`character`)\cr all statistics which can be selected here potentially.
350		#'
351		#' @return A `character` vector with the selected statistics.
352		#'
353		#' @keywords internal
354		afun_selected_stats <- function(.stats, all_stats) {
355	2x	checkmate::assert_character(.stats, null.ok = TRUE)
356	2x	checkmate::assert_character(all_stats)
357	2x	if (is.null(.stats)) {
358	1x	all_stats
359		} else {
360	1x	intersect(.stats, all_stats)
361		}
362		}
363
364		#' Add Variable Labels to Top Left Corner in Table
365		#'
366		#' @description `r lifecycle::badge("stable")`
367		#'
368		#' Helper layout creating function to just append the variable labels of a given variables vector
369		#' from a given dataset in the top left corner. If a variable label is not found then the
370		#' variable name itself is used instead. Multiple variable labels are concatenated with slashes.
371		#'
372		#' @inheritParams argument_convention
373		#' @param vars (`character`)\cr variable names of which the labels are to be looked up in `df`.
374		#' @param indent (`integer`)\cr non-negative number of nested indent space, default to 0L which means no indent.
375		#' 1L means two spaces indent, 2L means four spaces indent and so on.
376		#'
377		#' @return A modified layout with the new variable label(s) added to the top-left material.
378		#'
379		#' @note This is not an optimal implementation of course, since we are using here the data set
380		#' itself during the layout creation. When we have a more mature `rtables` implementation then
381		#' this will also be improved or not necessary anymore.
382		#'
383		#' @examples
384		#' lyt <- basic_table() %>%
385		#' split_cols_by("ARM") %>%
386		#' add_colcounts() %>%
387		#' split_rows_by("SEX") %>%
388		#' append_varlabels(DM, "SEX") %>%
389		#' analyze("AGE", afun = mean) %>%
390		#' append_varlabels(DM, "AGE", indent = 1)
391		#' build_table(lyt, DM)
392		#'
393		#' lyt <- basic_table() %>%
394		#' split_cols_by("ARM") %>%
395		#' split_rows_by("SEX") %>%
396		#' analyze("AGE", afun = mean) %>%
397		#' append_varlabels(DM, c("SEX", "AGE"))
398		#' build_table(lyt, DM)
399		#'
400		#' @export
401		append_varlabels <- function(lyt, df, vars, indent = 0L) {
402	3x	if (checkmate::test_flag(indent)) {
403	!	warning("indent argument is now accepting integers. Boolean indent will be converted to integers.")
404	!	indent <- as.integer(indent)
405		}
406
407	3x	checkmate::assert_data_frame(df)
408	3x	checkmate::assert_character(vars)
409	3x	checkmate::assert_count(indent)
410
411	3x	lab <- formatters::var_labels(df[vars], fill = TRUE)
412	3x	lab <- paste(lab, collapse = " / ")
413	3x	space <- paste(rep(" ", indent * 2), collapse = "")
414	3x	lab <- paste0(space, lab)
415
416	3x	append_topleft(lyt, lab)
417		}
418
419		#' Default string replacement for `NA` values
420		#'
421		#' @description `r lifecycle::badge("stable")`
422		#'
423		#' The default string used to represent `NA` values. This value is used as the default
424		#' value for the `na_str` argument throughout the `tern` package, and printed in place
425		#' of `NA` values in output tables. If not specified for each `tern` function by the user
426		#' via the `na_str` argument, or in the R environment options via [set_default_na_str()],
427		#' then `NA` is used.
428		#'
429		#' @param na_str (`string`)\cr Single string value to set in the R environment options as
430		#' the default value to replace `NA`s. Use `getOption("tern_default_na_str")` to check the
431		#' current value set in the R environment (defaults to `NULL` if not set).
432		#'
433		#' @name default_na_str
434		NULL
435
436		#' @describeIn default_na_str Getter for default `NA` value replacement string.
437		#'
438		#' @return
439		#' * `default_na_str` returns the current value if an R environment option has been set
440		#' for `"tern_default_na_str"`, or `NA_character_` otherwise.
441		#'
442		#' @examples
443		#' # Default settings
444		#' default_na_str()
445		#' getOption("tern_default_na_str")
446		#'
447		#' # Set custom value
448		#' set_default_na_str("<Missing>")
449		#'
450		#' # Settings after value has been set
451		#' default_na_str()
452		#' getOption("tern_default_na_str")
453		#'
454		#' @export
455		default_na_str <- function() {
456	226x	getOption("tern_default_na_str", default = NA_character_)
457		}
458
459		#' @describeIn default_na_str Setter for default `NA` value replacement string. Sets the
460		#' option `"tern_default_na_str"` within the R environment.
461		#'
462		#' @return
463		#' * `set_default_na_str` has no return value.
464		#'
465		#' @export
466		set_default_na_str <- function(na_str) {
467	3x	checkmate::assert_character(na_str, len = 1, null.ok = TRUE)
468	3x	options("tern_default_na_str" = na_str)
469		}

1		#' Add Titles, Footnotes, Page Number, and a Bounding Box to a Grid Grob
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' This function is useful to label grid grobs (also `ggplot2`, and `lattice` plots)
6		#' with title, footnote, and page numbers.
7		#'
8		#' @inheritParams grid::grob
9		#' @param grob a grid grob object, optionally `NULL` if only a `grob` with the decoration should be shown.
10		#' @param titles vector of character strings. Vector elements are separated by a newline and strings are wrapped
11		#' according to the page width.
12		#' @param footnotes vector of character string. Same rules as for `titles`.
13		#' @param page string with page numeration, if `NULL` then no page number is displayed.
14		#' @param width_titles unit object
15		#' @param width_footnotes unit object
16		#' @param border boolean, whether a a border should be drawn around the plot or not.
17		#' @param margins unit object of length 4
18		#' @param padding unit object of length 4
19		#' @param outer_margins unit object of length 4
20		#' @param gp_titles a `gpar` object
21		#' @param gp_footnotes a `gpar` object
22		#'
23		#' @return A grid grob (`gTree`).
24		#'
25		#' @details The titles and footnotes will be ragged, i.e. each title will be wrapped individually.
26		#'
27		#' @examples
28		#' library(grid)
29		#'
30		#' titles <- c(
31		#' "Edgar Anderson's Iris Data",
32		#' paste(
33		#' "This famous (Fisher's or Anderson's) iris data set gives the measurements",
34		#' "in centimeters of the variables sepal length and width and petal length",
35		#' "and width, respectively, for 50 flowers from each of 3 species of iris."
36		#' )
37		#' )
38		#'
39		#' footnotes <- c(
40		#' "The species are Iris setosa, versicolor, and virginica.",
41		#' paste(
42		#' "iris is a data frame with 150 cases (rows) and 5 variables (columns) named",
43		#' "Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species."
44		#' )
45		#' )
46		#'
47		#' ## empty plot
48		#' grid.newpage()
49		#'
50		#' grid.draw(
51		#' decorate_grob(
52		#' NULL,
53		#' titles = titles,
54		#' footnotes = footnotes,
55		#' page = "Page 4 of 10"
56		#' )
57		#' )
58		#'
59		#' # grid
60		#' p <- gTree(
61		#' children = gList(
62		#' rectGrob(),
63		#' xaxisGrob(),
64		#' yaxisGrob(),
65		#' textGrob("Sepal.Length", y = unit(-4, "lines")),
66		#' textGrob("Petal.Length", x = unit(-3.5, "lines"), rot = 90),
67		#' pointsGrob(iris$Sepal.Length, iris$Petal.Length, gp = gpar(col = iris$Species), pch = 16)
68		#' ),
69		#' vp = vpStack(plotViewport(), dataViewport(xData = iris$Sepal.Length, yData = iris$Petal.Length))
70		#' )
71		#' grid.newpage()
72		#' grid.draw(p)
73		#'
74		#' grid.newpage()
75		#' grid.draw(
76		#' decorate_grob(
77		#' grob = p,
78		#' titles = titles,
79		#' footnotes = footnotes,
80		#' page = "Page 6 of 129"
81		#' )
82		#' )
83		#'
84		#' ## with ggplot2
85		#' library(ggplot2)
86		#'
87		#' p_gg <- ggplot2::ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) +
88		#' ggplot2::geom_point()
89		#' p_gg
90		#' p <- ggplotGrob(p_gg)
91		#' grid.newpage()
92		#' grid.draw(
93		#' decorate_grob(
94		#' grob = p,
95		#' titles = titles,
96		#' footnotes = footnotes,
97		#' page = "Page 6 of 129"
98		#' )
99		#' )
100		#'
101		#' ## with lattice
102		#' library(lattice)
103		#'
104		#' xyplot(Sepal.Length ~ Petal.Length, data = iris, col = iris$Species)
105		#' p <- grid.grab()
106		#' grid.newpage()
107		#' grid.draw(
108		#' decorate_grob(
109		#' grob = p,
110		#' titles = titles,
111		#' footnotes = footnotes,
112		#' page = "Page 6 of 129"
113		#' )
114		#' )
115		#'
116		#' # with gridExtra - no borders
117		#' library(gridExtra)
118		#' grid.newpage()
119		#' grid.draw(
120		#' decorate_grob(
121		#' tableGrob(
122		#' head(mtcars)
123		#' ),
124		#' titles = "title",
125		#' footnotes = "footnote",
126		#' border = FALSE
127		#' )
128		#' )
129		#'
130		#' @export
131		decorate_grob <- function(grob,
132		titles,
133		footnotes,
134		page = "",
135		width_titles = grid::unit(1, "npc") - grid::unit(1.5, "cm"),
136		width_footnotes = grid::unit(1, "npc") - grid::unit(1.5, "cm"),
137		border = TRUE,
138		margins = grid::unit(c(1, 0, 1, 0), "lines"),
139		padding = grid::unit(rep(1, 4), "lines"),
140		outer_margins = grid::unit(c(2, 1.5, 3, 1.5), "cm"),
141		gp_titles = grid::gpar(),
142		gp_footnotes = grid::gpar(fontsize = 8),
143		name = NULL,
144		gp = grid::gpar(),
145		vp = NULL) {
146	9x	st_titles <- split_text_grob(
147	9x	titles,
148	9x	x = 0, y = 1,
149	9x	just = c("left", "top"),
150	9x	width = width_titles,
151	9x	vp = grid::viewport(layout.pos.row = 1, layout.pos.col = 1),
152	9x	gp = gp_titles
153		)
154
155	9x	st_footnotes <- split_text_grob(
156	9x	footnotes,
157	9x	x = 0, y = 1,
158	9x	just = c("left", "top"),
159	9x	width = width_footnotes,
160	9x	vp = grid::viewport(layout.pos.row = 3, layout.pos.col = 1),
161	9x	gp = gp_footnotes
162		)
163
164	9x	pg_footnote <- grid::textGrob(
165	9x	paste("\n", page),
166	9x	x = 1, y = 0,
167	9x	just = c("right", "bottom"),
168	9x	vp = grid::viewport(layout.pos.row = 4, layout.pos.col = 1),
169	9x	gp = gp_footnotes
170		)
171
172	9x	grid::gTree(
173	9x	grob = grob,
174	9x	titles = titles,
175	9x	footnotes = footnotes,
176	9x	page = page,
177	9x	width_titles = width_titles,
178	9x	width_footnotes = width_footnotes,
179	9x	border = border,
180	9x	margins = margins,
181	9x	padding = padding,
182	9x	outer_margins = outer_margins,
183	9x	gp_titles = gp_titles,
184	9x	gp_footnotes = gp_footnotes,
185	9x	children = grid::gList(
186	9x	grid::gTree(
187	9x	children = grid::gList(
188	9x	st_titles,
189	9x	grid::gTree(
190	9x	children = grid::gList(
191	9x	if (border) grid::rectGrob(),
192	9x	grid::gTree(
193	9x	children = grid::gList(
194	9x	grob
195		),
196	9x	vp = grid::plotViewport(margins = padding)
197		)
198		),
199	9x	vp = grid::vpStack(
200	9x	grid::viewport(layout.pos.row = 2, layout.pos.col = 1),
201	9x	grid::plotViewport(margins = margins)
202		)
203		),
204	9x	st_footnotes,
205	9x	pg_footnote
206		),
207	9x	childrenvp = NULL,
208	9x	name = "titles_grob_footnotes",
209	9x	vp = grid::vpStack(
210	9x	grid::plotViewport(margins = outer_margins),
211	9x	grid::viewport(
212	9x	layout = grid::grid.layout(
213	9x	nrow = 4, ncol = 1,
214	9x	heights = grid::unit.c(
215	9x	grid::grobHeight(st_titles),
216	9x	grid::unit(1, "null"),
217	9x	grid::grobHeight(st_footnotes),
218	9x	grid::grobHeight(pg_footnote)
219		)
220		)
221		)
222		)
223		)
224		),
225	9x	name = name,
226	9x	gp = gp,
227	9x	vp = vp,
228	9x	cl = "decoratedGrob"
229		)
230		}
231
232		#' @importFrom grid validDetails
233		#' @noRd
234		validDetails.decoratedGrob <- function(x) {
235	!	checkmate::assert_character(x$titles)
236	!	checkmate::assert_character(x$footnotes)
237
238	!	if (!is.null(x$grob)) {
239	!	checkmate::assert_true(grid::is.grob(x$grob))
240		}
241	!	if (length(x$page) == 1) {
242	!	checkmate::assert_character(x$page)
243		}
244	!	if (!grid::is.unit(x$outer_margins)) {
245	!	checkmate::assert_vector(x$outer_margins, len = 4)
246		}
247	!	if (!grid::is.unit(x$margins)) {
248	!	checkmate::assert_vector(x$margins, len = 4)
249		}
250	!	if (!grid::is.unit(x$padding)) {
251	!	checkmate::assert_vector(x$padding, len = 4)
252		}
253
254	!	x
255		}
256
257		#' @importFrom grid widthDetails
258		#' @noRd
259		widthDetails.decoratedGrob <- function(x) {
260	!	grid::unit(1, "null")
261		}
262
263		#' @importFrom grid heightDetails
264		#' @noRd
265		heightDetails.decoratedGrob <- function(x) {
266	!	grid::unit(1, "null")
267		}
268
269		# Adapted from Paul Murell R Graphics 2nd Edition
270		# https://www.stat.auckland.ac.nz/~paul/RG2e/interactgrid-splittext.R
271		split_string <- function(text, width) {
272	19x	strings <- strsplit(text, " ")
273	19x	out_string <- NA
274	19x	for (string_i in seq_along(strings)) {
275	19x	newline_str <- strings[[string_i]]
276	6x	if (length(newline_str) == 0) newline_str <- ""
277	19x	if (is.na(out_string[string_i])) {
278	19x	out_string[string_i] <- newline_str[[1]][[1]]
279	19x	linewidth <- grid::stringWidth(out_string[string_i])
280		}
281	19x	gapwidth <- grid::stringWidth(" ")
282	19x	availwidth <- as.numeric(width)
283	19x	if (length(newline_str) > 1) {
284	7x	for (i in seq(2, length(newline_str))) {
285	83x	width_i <- grid::stringWidth(newline_str[i])
286	83x	if (grid::convertWidth(linewidth + gapwidth + width_i, grid::unitType(width), valueOnly = TRUE) < availwidth) {
287	78x	sep <- " "
288	78x	linewidth <- linewidth + gapwidth + width_i
289		} else {
290	5x	sep <- "\n"
291	5x	linewidth <- width_i
292		}
293	83x	out_string[string_i] <- paste(out_string[string_i], newline_str[i], sep = sep)
294		}
295		}
296		}
297	19x	paste(out_string, collapse = "\n")
298		}
299
300		#' Split Text According To Available Text Width
301		#'
302		#' Dynamically wrap text.
303		#'
304		#' @inheritParams grid::grid.text
305		#' @param text character string
306		#' @param width a unit object specifying max width of text
307		#'
308		#' @return A text grob.
309		#'
310		#' @details This code is taken from `R Graphics by Paul Murell, 2nd edition`
311		#'
312		#' @keywords internal
313		split_text_grob <- function(text,
314		x = grid::unit(0.5, "npc"),
315		y = grid::unit(0.5, "npc"),
316		width = grid::unit(1, "npc"),
317		just = "centre",
318		hjust = NULL,
319		vjust = NULL,
320		default.units = "npc", # nolint
321		name = NULL,
322		gp = grid::gpar(),
323		vp = NULL) {
324	18x	if (!grid::is.unit(x)) x <- grid::unit(x, default.units)
325	18x	if (!grid::is.unit(y)) y <- grid::unit(y, default.units)
326	!	if (!grid::is.unit(width)) width <- grid::unit(width, default.units)
327	!	if (grid::unitType(x) %in% c("sum", "min", "max")) x <- grid::convertUnit(x, default.units)
328	!	if (grid::unitType(y) %in% c("sum", "min", "max")) y <- grid::convertUnit(y, default.units)
329	18x	if (grid::unitType(width) %in% c("sum", "min", "max")) width <- grid::convertUnit(width, default.units)
330
331	18x	if (length(gp) > 0) { # account for effect of gp on text width
332	9x	width <- width * grid::convertWidth(grid::grobWidth(grid::textGrob(text)), "npc", valueOnly = TRUE) /
333	9x	grid::convertWidth(grid::grobWidth(grid::textGrob(text, gp = gp)), "npc", valueOnly = TRUE)
334		}
335
336		## if it is a fixed unit then we do not need to recalculate when viewport resized
337	18x	if (!inherits(width, "unit.arithmetic") && !is.null(attr(width, "unit")) &&
338	18x	attr(width, "unit") %in% c("cm", "inches", "mm", "points", "picas", "bigpts", "dida", "cicero", "scaledpts")) { # nolint
339	!	attr(text, "fixed_text") <- paste(vapply(text, split_string, character(1), width = width), collapse = "\n")
340		}
341
342	18x	grid::grid.text(
343	18x	label = split_string(text, width),
344	18x	x = x, y = y,
345	18x	just = just,
346	18x	hjust = hjust,
347	18x	vjust = vjust,
348	18x	rot = 0,
349	18x	check.overlap = FALSE,
350	18x	name = name,
351	18x	gp = gp,
352	18x	vp = vp,
353	18x	draw = FALSE
354		)
355		}
356
357		#' @importFrom grid validDetails
358		#' @noRd
359		validDetails.dynamicSplitText <- function(x) {
360	!	checkmate::assert_character(x$text)
361	!	checkmate::assert_true(grid::is.unit(x$width))
362	!	checkmate::assert_vector(x$width, len = 1)
363	!	x
364		}
365
366		#' @importFrom grid heightDetails
367		#' @noRd
368		heightDetails.dynamicSplitText <- function(x) {
369	!	txt <- if (!is.null(attr(x$text, "fixed_text"))) {
370	!	attr(x$text, "fixed_text")
371		} else {
372	!	paste(vapply(x$text, split_string, character(1), width = x$width), collapse = "\n")
373		}
374	!	grid::stringHeight(txt)
375		}
376
377		#' @importFrom grid widthDetails
378		#' @noRd
379		widthDetails.dynamicSplitText <- function(x) {
380	!	x$width
381		}
382
383		#' @importFrom grid drawDetails
384		#' @noRd
385		drawDetails.dynamicSplitText <- function(x, recording) {
386	!	txt <- if (!is.null(attr(x$text, "fixed_text"))) {
387	!	attr(x$text, "fixed_text")
388		} else {
389	!	paste(vapply(x$text, split_string, character(1), width = x$width), collapse = "\n")
390		}
391
392	!	x$width <- NULL
393	!	x$label <- txt
394	!	x$text <- NULL
395	!	class(x) <- c("text", class(x)[-1])
396
397	!	grid::grid.draw(x)
398		}
399
400		#' Update Page Number
401		#'
402		#' Automatically updates page number.
403		#'
404		#' @param npages number of pages in total
405		#' @param ... passed on to [decorate_grob()]
406		#'
407		#' @return Closure that increments the page number.
408		#'
409		#' @keywords internal
410		decorate_grob_factory <- function(npages, ...) {
411	2x	current_page <- 0
412	2x	function(grob) {
413	7x	current_page <<- current_page + 1
414	7x	if (current_page > npages) {
415	1x	stop(paste("current page is", current_page, "but max.", npages, "specified."))
416		}
417	6x	decorate_grob(grob = grob, page = paste("Page", current_page, "of", npages), ...)
418		}
419		}
420
421		#' Decorate Set of `grobs` and Add Page Numbering
422		#'
423		#' @description `r lifecycle::badge("stable")`
424		#'
425		#' Note that this uses the [decorate_grob_factory()] function.
426		#'
427		#' @param grobs a list of grid grobs
428		#' @param ... arguments passed on to [decorate_grob()].
429		#'
430		#' @return A decorated grob.
431		#'
432		#' @examples
433		#' library(ggplot2)
434		#' library(grid)
435		#' g <- with(data = iris, {
436		#' list(
437		#' ggplot2::ggplotGrob(
438		#' ggplot2::ggplot(mapping = aes(Sepal.Length, Sepal.Width, col = Species)) +
439		#' ggplot2::geom_point()
440		#' ),
441		#' ggplot2::ggplotGrob(
442		#' ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Length, col = Species)) +
443		#' ggplot2::geom_point()
444		#' ),
445		#' ggplot2::ggplotGrob(
446		#' ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Width, col = Species)) +
447		#' ggplot2::geom_point()
448		#' ),
449		#' ggplot2::ggplotGrob(
450		#' ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Length, col = Species)) +
451		#' ggplot2::geom_point()
452		#' ),
453		#' ggplot2::ggplotGrob(
454		#' ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Width, col = Species)) +
455		#' ggplot2::geom_point()
456		#' ),
457		#' ggplot2::ggplotGrob(
458		#' ggplot2::ggplot(mapping = aes(Petal.Length, Petal.Width, col = Species)) +
459		#' ggplot2::geom_point()
460		#' )
461		#' )
462		#' })
463		#' lg <- decorate_grob_set(grobs = g, titles = "Hello\nOne\nTwo\nThree", footnotes = "")
464		#'
465		#' draw_grob(lg[[1]])
466		#' draw_grob(lg[[2]])
467		#' draw_grob(lg[[6]])
468		#'
469		#' @export
470		decorate_grob_set <- function(grobs, ...) {
471	1x	n <- length(grobs)
472	1x	lgf <- decorate_grob_factory(npages = n, ...)
473	1x	lapply(grobs, lgf)
474		}

1		#' Helper Function to create a new `SMQ` variable in `ADAE` by stacking `SMQ` and/or `CQ` records.
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper Function to create a new `SMQ` variable in `ADAE` that consists of all adverse events belonging to
6		#' selected Standardized/Customized queries. The new dataset will only contain records of the adverse events
7		#' belonging to any of the selected baskets. Remember that `na_str` must match the needed pre-processing
8		#' done with [df_explicit_na()] to have the desired output.
9		#'
10		#' @inheritParams argument_convention
11		#' @param baskets (`character`)\cr variable names of the selected Standardized/Customized queries.
12		#' @param smq_varlabel (`string`)\cr a label for the new variable created.
13		#' @param keys (`character`)\cr names of the key variables to be returned along with the new variable created.
14		#' @param aag_summary (`data.frame`)\cr containing the `SMQ` baskets and the levels of interest for the final `SMQ`
15		#' variable. This is useful when there are some levels of interest that are not observed in the `df` dataset.
16		#' The two columns of this dataset should be named `basket` and `basket_name`.
17		#'
18		#' @return `data.frame` with variables in `keys` taken from `df` and new variable `SMQ` containing
19		#' records belonging to the baskets selected via the `baskets` argument.
20		#'
21		#' @examples
22		#' adae <- tern_ex_adae[1:20, ] %>% df_explicit_na()
23		#' h_stack_by_baskets(df = adae)
24		#'
25		#' aag <- data.frame(
26		#' NAMVAR = c("CQ01NAM", "CQ02NAM", "SMQ01NAM", "SMQ02NAM"),
27		#' REFNAME = c(
28		#' "D.2.1.5.3/A.1.1.1.1 aesi", "X.9.9.9.9/Y.8.8.8.8 aesi",
29		#' "C.1.1.1.3/B.2.2.3.1 aesi", "C.1.1.1.3/B.3.3.3.3 aesi"
30		#' ),
31		#' SCOPE = c("", "", "BROAD", "BROAD"),
32		#' stringsAsFactors = FALSE
33		#' )
34		#'
35		#' basket_name <- character(nrow(aag))
36		#' cq_pos <- grep("^(CQ).+NAM$", aag$NAMVAR)
37		#' smq_pos <- grep("^(SMQ).+NAM$", aag$NAMVAR)
38		#' basket_name[cq_pos] <- aag$REFNAME[cq_pos]
39		#' basket_name[smq_pos] <- paste0(
40		#' aag$REFNAME[smq_pos], "(", aag$SCOPE[smq_pos], ")"
41		#' )
42		#'
43		#' aag_summary <- data.frame(
44		#' basket = aag$NAMVAR,
45		#' basket_name = basket_name,
46		#' stringsAsFactors = TRUE
47		#' )
48		#'
49		#' result <- h_stack_by_baskets(df = adae, aag_summary = aag_summary)
50		#' all(levels(aag_summary$basket_name) %in% levels(result$SMQ))
51		#'
52		#' h_stack_by_baskets(
53		#' df = adae,
54		#' aag_summary = NULL,
55		#' keys = c("STUDYID", "USUBJID", "AEDECOD", "ARM"),
56		#' baskets = "SMQ01NAM"
57		#' )
58		#'
59		#' @export
60		h_stack_by_baskets <- function(df,
61		baskets = grep("^(SMQ\|CQ).+NAM$", names(df), value = TRUE),
62		smq_varlabel = "Standardized MedDRA Query",
63		keys = c("STUDYID", "USUBJID", "ASTDTM", "AEDECOD", "AESEQ"),
64		aag_summary = NULL,
65		na_str = "<Missing>") {
66	5x	smq_nam <- baskets[startsWith(baskets, "SMQ")]
67		# SC corresponding to NAM
68	5x	smq_sc <- gsub(pattern = "NAM", replacement = "SC", x = smq_nam, fixed = TRUE)
69	5x	smq <- stats::setNames(smq_sc, smq_nam)
70
71	5x	checkmate::assert_character(baskets)
72	5x	checkmate::assert_string(smq_varlabel)
73	5x	checkmate::assert_data_frame(df)
74	5x	checkmate::assert_true(all(startsWith(baskets, "SMQ") \| startsWith(baskets, "CQ")))
75	4x	checkmate::assert_true(all(endsWith(baskets, "NAM")))
76	3x	checkmate::assert_subset(baskets, names(df))
77	3x	checkmate::assert_subset(keys, names(df))
78	3x	checkmate::assert_subset(smq_sc, names(df))
79	3x	checkmate::assert_string(na_str)
80
81	3x	if (!is.null(aag_summary)) {
82	1x	assert_df_with_variables(
83	1x	df = aag_summary,
84	1x	variables = list(val = c("basket", "basket_name"))
85		)
86		# Warning in case there is no match between `aag_summary$basket` and `baskets` argument.
87		# Honestly, I think those should completely match. Target baskets should be the same.
88	1x	if (length(intersect(baskets, unique(aag_summary$basket))) == 0) {
89	!	warning("There are 0 baskets in common between aag_summary$basket and `baskets` argument.")
90		}
91		}
92
93	3x	var_labels <- c(formatters::var_labels(df[, keys]), "SMQ" = smq_varlabel)
94
95		# convert `na_str` records from baskets to NA for the later loop and from wide to long steps
96	3x	df[, c(baskets, smq_sc)][df[, c(baskets, smq_sc)] == na_str] <- NA
97
98	3x	if (all(is.na(df[, baskets]))) { # in case there is no level for the target baskets
99	1x	df_long <- df[-seq_len(nrow(df)), keys] # we just need an empty dataframe keeping all factor levels
100		} else {
101		# Concatenate SMQxxxNAM with corresponding SMQxxxSC
102	2x	df_cnct <- df[, c(keys, baskets[startsWith(baskets, "CQ")])]
103
104	2x	for (nam in names(smq)) {
105	4x	sc <- smq[nam] # SMQxxxSC corresponding to SMQxxxNAM
106	4x	nam_notna <- !is.na(df[[nam]])
107	4x	new_colname <- paste(nam, sc, sep = "_")
108	4x	df_cnct[nam_notna, new_colname] <- paste0(df[[nam]], "(", df[[sc]], ")")[nam_notna]
109		}
110
111	2x	df_cnct$unique_id <- seq(1, nrow(df_cnct))
112	2x	var_cols <- names(df_cnct)[!(names(df_cnct) %in% c(keys, "unique_id"))]
113		# have to convert df_cnct from tibble to dataframe
114		# as it throws a warning otherwise about rownames.
115		# tibble do not support rownames and reshape creates rownames
116
117	2x	df_long <- stats::reshape(
118	2x	data = as.data.frame(df_cnct),
119	2x	varying = var_cols,
120	2x	v.names = "SMQ",
121	2x	idvar = names(df_cnct)[names(df_cnct) %in% c(keys, "unique_id")],
122	2x	direction = "long",
123	2x	new.row.names = seq(prod(length(var_cols), nrow(df_cnct)))
124		)
125
126	2x	df_long <- df_long[!is.na(df_long[, "SMQ"]), !(names(df_long) %in% c("time", "unique_id"))]
127	2x	df_long$SMQ <- as.factor(df_long$SMQ)
128		}
129
130	3x	smq_levels <- setdiff(levels(df_long[["SMQ"]]), na_str)
131
132	3x	if (!is.null(aag_summary)) {
133		# A warning in case there is no match between df and aag_summary records
134	1x	if (length(intersect(smq_levels, unique(aag_summary$basket_name))) == 0) {
135	1x	warning("There are 0 basket levels in common between aag_summary$basket_name and df.")
136		}
137	1x	df_long[["SMQ"]] <- factor(
138	1x	df_long[["SMQ"]],
139	1x	levels = sort(
140	1x	c(
141	1x	smq_levels,
142	1x	setdiff(unique(aag_summary$basket_name), smq_levels)
143		)
144		)
145		)
146		} else {
147	2x	all_na_basket_flag <- vapply(df[, baskets], function(x) {
148	6x	all(is.na(x))
149	2x	}, FUN.VALUE = logical(1))
150	2x	all_na_basket <- baskets[all_na_basket_flag]
151
152	2x	df_long[["SMQ"]] <- factor(
153	2x	df_long[["SMQ"]],
154	2x	levels = sort(c(smq_levels, all_na_basket))
155		)
156		}
157	3x	formatters::var_labels(df_long) <- var_labels
158	3x	tibble::tibble(df_long)
159		}

1		#' Re-implemented [range()] Default S3 method for numerical objects
2		#'
3		#' This function returns `c(NA, NA)` instead of `c(-Inf, Inf)` for zero-length data
4		#' without any warnings.
5		#'
6		#' @param x (`numeric`)\cr a sequence of numbers for which the range is computed.
7		#' @param na.rm (`logical`)\cr indicating if `NA` should be omitted.
8		#' @param finite (`logical`)\cr indicating if non-finite elements should be removed.
9		#'
10		#' @return A 2-element vector of class `numeric`.
11		#'
12		#' @keywords internal
13		range_noinf <- function(x, na.rm = FALSE, finite = FALSE) { # nolint
14
15	942x	checkmate::assert_numeric(x)
16
17	942x	if (finite) {
18	24x	x <- x[is.finite(x)] # removes NAs too
19	918x	} else if (na.rm) {
20	558x	x <- x[!is.na(x)]
21		}
22
23	942x	if (length(x) == 0) {
24	49x	rval <- c(NA, NA)
25	49x	mode(rval) <- typeof(x)
26		} else {
27	893x	rval <- c(min(x, na.rm = FALSE), max(x, na.rm = FALSE))
28		}
29
30	942x	return(rval)
31		}
32
33		#' Utility function to create label for confidence interval
34		#'
35		#' @description `r lifecycle::badge("stable")`
36		#'
37		#' @inheritParams argument_convention
38		#'
39		#' @return A `string`.
40		#'
41		#' @export
42		f_conf_level <- function(conf_level) {
43	1488x	assert_proportion_value(conf_level)
44	1486x	paste0(conf_level * 100, "% CI")
45		}
46
47		#' Utility function to create label for p-value
48		#'
49		#' @description `r lifecycle::badge("stable")`
50		#'
51		#' @param test_mean (`number`)\cr mean value to test under the null hypothesis.
52		#'
53		#' @return A `string`.
54		#'
55		#' @export
56		f_pval <- function(test_mean) {
57	352x	checkmate::assert_numeric(test_mean, len = 1)
58	350x	paste0("p-value (H0: mean = ", test_mean, ")")
59		}
60
61		#' Utility function to return a named list of covariate names.
62		#'
63		#' @param covariates (`character`)\cr a vector that can contain single variable names (such as
64		#' `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
65		#'
66		#' @return A named `list` of `character` vector.
67		#'
68		#' @keywords internal
69		get_covariates <- function(covariates) {
70	14x	checkmate::assert_character(covariates)
71	12x	cov_vars <- unique(trimws(unlist(strsplit(covariates, "\\*"))))
72	12x	stats::setNames(as.list(cov_vars), cov_vars)
73		}
74
75		#' Replicate Entries of a Vector if Required
76		#'
77		#' @description `r lifecycle::badge("stable")`
78		#'
79		#' Replicate entries of a vector if required.
80		#'
81		#' @inheritParams argument_convention
82		#' @param n (`count`)\cr how many entries we need.
83		#'
84		#' @return `x` if it has the required length already or is `NULL`,
85		#' otherwise if it is scalar the replicated version of it with `n` entries.
86		#'
87		#' @note This function will fail if `x` is not of length `n` and/or is not a scalar.
88		#'
89		#' @export
90		to_n <- function(x, n) {
91	1x	if (is.null(x)) {
92	!	NULL
93	1x	} else if (length(x) == 1) {
94	!	rep(x, n)
95	1x	} else if (length(x) == n) {
96	1x	x
97		} else {
98	!	stop("dimension mismatch")
99		}
100		}
101
102		#' Check Element Dimension
103		#'
104		#' Checks if the elements in `...` have the same dimension.
105		#'
106		#' @param ... (`data.frame`s or `vector`s)\cr any data frames/vectors.
107		#' @param omit_null (`logical`)\cr whether `NULL` elements in `...` should be omitted from the check.
108		#'
109		#' @return A `logical` value.
110		#'
111		#' @keywords internal
112		check_same_n <- function(..., omit_null = TRUE) {
113	2x	dots <- list(...)
114
115	2x	n_list <- Map(
116	2x	function(x, name) {
117	5x	if (is.null(x)) {
118	!	if (omit_null) {
119	2x	NA_integer_
120		} else {
121	!	stop("arg", name, "is not supposed to be NULL")
122		}
123	5x	} else if (is.data.frame(x)) {
124	!	nrow(x)
125	5x	} else if (is.atomic(x)) {
126	5x	length(x)
127		} else {
128	!	stop("data structure for ", name, "is currently not supported")
129		}
130		},
131	2x	dots, names(dots)
132		)
133
134	2x	n <- stats::na.omit(unlist(n_list))
135
136	2x	if (length(unique(n)) > 1) {
137	!	sel <- which(n != n[1])
138	!	stop("Dimension mismatch:", paste(names(n)[sel], collapse = ", "), " do not have N=", n[1])
139		}
140
141	2x	TRUE
142		}
143
144		#' Utility function to check if a float is equal to another float
145		#'
146		#' @description Uses `.Machine$double.eps` as the tolerance for the comparison.
147		#'
148		#' @param x (`float`)\cr A single number.
149		#' @param y (`float`)\cr A single number.
150		#'
151		#' @return `TRUE`, if identical. `FALSE`, otherwise
152		#'
153		#' @keywords internal
154		.is_equal_float <- function(x, y) {
155	1919x	checkmate::assert_number(x)
156	1919x	checkmate::assert_number(y)
157
158		# Define a tolerance
159	1919x	tolerance <- .Machine$double.eps
160
161		# Check if x is close enough to y
162	1919x	abs(x - y) < tolerance
163		}
164
165		#' Make Names Without Dots
166		#'
167		#' @param nams (`character`)\cr vector of original names.
168		#'
169		#' @return A `character` `vector` of proper names, which does not use dots in contrast to [make.names()].
170		#'
171		#' @keywords internal
172		make_names <- function(nams) {
173	6x	orig <- make.names(nams)
174	6x	gsub(".", "", x = orig, fixed = TRUE)
175		}
176
177		#' Conversion of Months to Days
178		#'
179		#' @description `r lifecycle::badge("stable")`
180		#'
181		#' Conversion of Months to Days. This is an approximative calculation because it
182		#' considers each month as having an average of 30.4375 days.
183		#'
184		#' @param x (`numeric`)\cr time in months.
185		#'
186		#' @return A `numeric` vector with the time in days.
187		#'
188		#' @examples
189		#' x <- c(13.25, 8.15, 1, 2.834)
190		#' month2day(x)
191		#'
192		#' @export
193		month2day <- function(x) {
194	1x	checkmate::assert_numeric(x)
195	1x	x * 30.4375
196		}
197
198		#' Conversion of Days to Months
199		#'
200		#' @param x (`numeric`)\cr time in days.
201		#'
202		#' @return A `numeric` vector with the time in months.
203		#'
204		#' @examples
205		#' x <- c(403, 248, 30, 86)
206		#' day2month(x)
207		#'
208		#' @export
209		day2month <- function(x) {
210	19x	checkmate::assert_numeric(x)
211	19x	x / 30.4375
212		}
213
214		#' Return an empty numeric if all elements are `NA`.
215		#'
216		#' @param x (`numeric`)\cr vector.
217		#'
218		#' @return An empty `numeric` if all elements of `x` are `NA`, otherwise `x`.
219		#'
220		#' @examples
221		#' x <- c(NA, NA, NA)
222		#' # Internal function - empty_vector_if_na
223		#' @keywords internal
224		empty_vector_if_na <- function(x) {
225	683x	if (all(is.na(x))) {
226	220x	numeric()
227		} else {
228	463x	x
229		}
230		}
231
232		#' Combine Two Vectors Element Wise
233		#'
234		#' @param x (`vector`)\cr first vector to combine.
235		#' @param y (`vector`)\cr second vector to combine.
236		#'
237		#' @return A `list` where each element combines corresponding elements of `x` and `y`.
238		#'
239		#' @examples
240		#' combine_vectors(1:3, 4:6)
241		#'
242		#' @export
243		combine_vectors <- function(x, y) {
244	70x	checkmate::assert_vector(x)
245	70x	checkmate::assert_vector(y, len = length(x))
246
247	70x	result <- lapply(as.data.frame(rbind(x, y)), `c`)
248	70x	names(result) <- NULL
249	70x	result
250		}
251
252		#' Extract Elements by Name
253		#'
254		#' This utility function extracts elements from a vector `x` by `names`.
255		#' Differences to the standard `[` function are:
256		#'
257		#' - If `x` is `NULL`, then still always `NULL` is returned (same as in base function).
258		#' - If `x` is not `NULL`, then the intersection of its names is made with `names` and those
259		#' elements are returned. That is, `names` which don't appear in `x` are not returned as `NA`s.
260		#'
261		#' @param x (named `vector`)\cr where to extract named elements from.
262		#' @param names (`character`)\cr vector of names to extract.
263		#'
264		#' @return `NULL` if `x` is `NULL`, otherwise the extracted elements from `x`.
265		#'
266		#' @keywords internal
267		extract_by_name <- function(x, names) {
268	3x	if (is.null(x)) {
269	1x	return(NULL)
270		}
271	2x	checkmate::assert_named(x)
272	2x	checkmate::assert_character(names)
273	2x	which_extract <- intersect(names(x), names)
274	2x	if (length(which_extract) > 0) {
275	1x	x[which_extract]
276		} else {
277	1x	NULL
278		}
279		}
280
281		#' Labels for Adverse Event Baskets
282		#'
283		#' @description `r lifecycle::badge("stable")`
284		#'
285		#' @param aesi (`character`)\cr with standardized `MedDRA` query name (e.g. `SMQzzNAM`) or customized query
286		#' name (e.g. `CQzzNAM`).
287		#' @param scope (`character`)\cr with scope of query (e.g. `SMQzzSC`).
288		#'
289		#' @return A `string` with the standard label for the `AE` basket.
290		#'
291		#' @examples
292		#' adae <- tern_ex_adae
293		#'
294		#' # Standardized query label includes scope.
295		#' aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC)
296		#'
297		#' # Customized query label.
298		#' aesi_label(adae$CQ01NAM)
299		#'
300		#' @export
301		aesi_label <- function(aesi, scope = NULL) {
302	3x	checkmate::assert_character(aesi)
303	3x	checkmate::assert_character(scope, null.ok = TRUE)
304	3x	aesi_label <- obj_label(aesi)
305	3x	aesi <- sas_na(aesi)
306	3x	aesi <- unique(aesi)[!is.na(unique(aesi))]
307
308	3x	lbl <- if (length(aesi) == 1 && !is.null(scope)) {
309	1x	scope <- sas_na(scope)
310	1x	scope <- unique(scope)[!is.na(unique(scope))]
311	1x	checkmate::assert_string(scope)
312	1x	paste0(aesi, " (", scope, ")")
313	3x	} else if (length(aesi) == 1 && is.null(scope)) {
314	1x	aesi
315		} else {
316	1x	aesi_label
317		}
318
319	3x	lbl
320		}
321
322		#' Indicate Study Arm Variable in Formula
323		#'
324		#' We use `study_arm` to indicate the study arm variable in `tern` formulas.
325		#'
326		#' @param x arm information
327		#'
328		#' @return `x`
329		#'
330		#' @keywords internal
331		study_arm <- function(x) {
332	!	structure(x, varname = deparse(substitute(x)))
333		}
334
335		#' Smooth Function with Optional Grouping
336		#'
337		#' @description `r lifecycle::badge("stable")`
338		#'
339		#' This produces `loess` smoothed estimates of `y` with Student confidence intervals.
340		#'
341		#' @param df (`data.frame`)\cr data set containing all analysis variables.
342		#' @param x (`character`)\cr value with x column name.
343		#' @param y (`character`)\cr value with y column name.
344		#' @param groups (`character`)\cr vector with optional grouping variables names.
345		#' @param level (`numeric`)\cr level of confidence interval to use (0.95 by default).
346		#'
347		#' @return A `data.frame` with original `x`, smoothed `y`, `ylow`, and `yhigh`, and
348		#' optional `groups` variables formatted as `factor` type.
349		#'
350		#' @export
351		get_smooths <- function(df, x, y, groups = NULL, level = 0.95) {
352	5x	checkmate::assert_data_frame(df)
353	5x	df_cols <- colnames(df)
354	5x	checkmate::assert_string(x)
355	5x	checkmate::assert_subset(x, df_cols)
356	5x	checkmate::assert_numeric(df[[x]])
357	5x	checkmate::assert_string(y)
358	5x	checkmate::assert_subset(y, df_cols)
359	5x	checkmate::assert_numeric(df[[y]])
360
361	5x	if (!is.null(groups)) {
362	4x	checkmate::assert_character(groups)
363	4x	checkmate::assert_subset(groups, df_cols)
364		}
365
366	5x	smooths <- function(x, y) {
367	18x	stats::predict(stats::loess(y ~ x), se = TRUE)
368		}
369
370	5x	if (!is.null(groups)) {
371	4x	cc <- stats::complete.cases(df[c(x, y, groups)])
372	4x	df_c <- df[cc, c(x, y, groups)]
373	4x	df_c_ordered <- df_c[do.call("order", as.list(df_c[, groups, drop = FALSE])), , drop = FALSE]
374	4x	df_c_g <- data.frame(Map(as.factor, df_c_ordered[groups]))
375
376	4x	df_smooth_raw <-
377	4x	by(df_c_ordered, df_c_g, function(d) {
378	17x	plx <- smooths(d[[x]], d[[y]])
379	17x	data.frame(
380	17x	x = d[[x]],
381	17x	y = plx$fit,
382	17x	ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
383	17x	yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
384		)
385		})
386
387	4x	df_smooth <- do.call(rbind, df_smooth_raw)
388	4x	df_smooth[groups] <- df_c_g
389
390	4x	df_smooth
391		} else {
392	1x	cc <- stats::complete.cases(df[c(x, y)])
393	1x	df_c <- df[cc, ]
394	1x	plx <- smooths(df_c[[x]], df_c[[y]])
395
396	1x	df_smooth <- data.frame(
397	1x	x = df_c[[x]],
398	1x	y = plx$fit,
399	1x	ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
400	1x	yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
401		)
402
403	1x	df_smooth
404		}
405		}
406
407		#' Number of Available (Non-Missing Entries) in a Vector
408		#'
409		#' Small utility function for better readability.
410		#'
411		#' @param x (`any`)\cr vector in which to count non-missing values.
412		#'
413		#' @return Number of non-missing values.
414		#'
415		#' @keywords internal
416		n_available <- function(x) {
417	254x	sum(!is.na(x))
418		}
419
420		#' Reapply Variable Labels
421		#'
422		#' This is a helper function that is used in tests.
423		#'
424		#' @param x (`vector`)\cr vector of elements that needs new labels.
425		#' @param varlabels (`character`)\cr vector of labels for `x`.
426		#' @param ... further parameters to be added to the list.
427		#'
428		#' @return `x` with variable labels reapplied.
429		#'
430		#' @export
431		reapply_varlabels <- function(x, varlabels, ...) {
432	10x	named_labels <- c(as.list(varlabels), list(...))
433	10x	formatters::var_labels(x)[names(named_labels)] <- as.character(named_labels)
434	10x	x
435		}
436
437		# Wrapper function of survival::clogit so that when model fitting failed, a more useful message would show
438		clogit_with_tryCatch <- function(formula, data, ...) { # nolint
439	30x	tryCatch(
440	30x	survival::clogit(formula = formula, data = data, ...),
441	30x	error = function(e) stop("model not built successfully with survival::clogit")
442		)
443		}

1		#' Difference Test for Two Proportions
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Various tests were implemented to test the difference between two proportions.
6		#'
7		#' @inheritParams argument_convention
8		#' @param method (`string`)\cr one of `chisq`, `cmh`, `fisher`, or `schouten`; specifies the test used
9		#' to calculate the p-value.
10		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("test_proportion_diff")`
11		#' to see available statistics for this function.
12		#'
13		#' @seealso [h_prop_diff_test]
14		#'
15		#' @name prop_diff_test
16		#' @order 1
17		NULL
18
19		#' @describeIn prop_diff_test Statistics function which tests the difference between two proportions.
20		#'
21		#' @return
22		#' * `s_test_proportion_diff()` returns a named `list` with a single item `pval` with an attribute `label`
23		#' describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same.
24		#'
25		#' @keywords internal
26		s_test_proportion_diff <- function(df,
27		.var,
28		.ref_group,
29		.in_ref_col,
30		variables = list(strata = NULL),
31		method = c("chisq", "schouten", "fisher", "cmh")) {
32	35x	method <- match.arg(method)
33	35x	y <- list(pval = "")
34
35	35x	if (!.in_ref_col) {
36	35x	assert_df_with_variables(df, list(rsp = .var))
37	35x	assert_df_with_variables(.ref_group, list(rsp = .var))
38	35x	rsp <- factor(
39	35x	c(.ref_group[[.var]], df[[.var]]),
40	35x	levels = c("TRUE", "FALSE")
41		)
42	35x	grp <- factor(
43	35x	rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
44	35x	levels = c("ref", "Not-ref")
45		)
46
47	35x	if (!is.null(variables$strata) \|\| method == "cmh") {
48	12x	strata <- variables$strata
49	12x	checkmate::assert_false(is.null(strata))
50	12x	strata_vars <- stats::setNames(as.list(strata), strata)
51	12x	assert_df_with_variables(df, strata_vars)
52	12x	assert_df_with_variables(.ref_group, strata_vars)
53	12x	strata <- c(interaction(.ref_group[strata]), interaction(df[strata]))
54		}
55
56	35x	tbl <- switch(method,
57	35x	cmh = table(grp, rsp, strata),
58	35x	table(grp, rsp)
59		)
60
61	35x	y$pval <- switch(method,
62	35x	chisq = prop_chisq(tbl),
63	35x	cmh = prop_cmh(tbl),
64	35x	fisher = prop_fisher(tbl),
65	35x	schouten = prop_schouten(tbl)
66		)
67		}
68
69	35x	y$pval <- formatters::with_label(y$pval, d_test_proportion_diff(method))
70	35x	y
71		}
72
73		#' Description of the Difference Test Between Two Proportions
74		#'
75		#' @description `r lifecycle::badge("stable")`
76		#'
77		#' This is an auxiliary function that describes the analysis in `s_test_proportion_diff`.
78		#'
79		#' @inheritParams s_test_proportion_diff
80		#'
81		#' @return `string` describing the test from which the p-value is derived.
82		#'
83		#' @export
84		d_test_proportion_diff <- function(method) {
85	49x	checkmate::assert_string(method)
86	49x	meth_part <- switch(method,
87	49x	"schouten" = "Chi-Squared Test with Schouten Correction",
88	49x	"chisq" = "Chi-Squared Test",
89	49x	"cmh" = "Cochran-Mantel-Haenszel Test",
90	49x	"fisher" = "Fisher's Exact Test",
91	49x	stop(paste(method, "does not have a description"))
92		)
93	49x	paste0("p-value (", meth_part, ")")
94		}
95
96		#' @describeIn prop_diff_test Formatted analysis function which is used as `afun` in `test_proportion_diff()`.
97		#'
98		#' @return
99		#' * `a_test_proportion_diff()` returns the corresponding list with formatted [rtables::CellValue()].
100		#'
101		#' @keywords internal
102		a_test_proportion_diff <- make_afun(
103		s_test_proportion_diff,
104		.formats = c(pval = "x.xxxx \| (<0.0001)"),
105		.indent_mods = c(pval = 1L)
106		)
107
108		#' @describeIn prop_diff_test Layout-creating function which can take statistics function arguments
109		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
110		#'
111		#' @return
112		#' * `test_proportion_diff()` returns a layout object suitable for passing to further layouting functions,
113		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
114		#' the statistics from `s_test_proportion_diff()` to the table layout.
115		#'
116		#' @examples
117		#' dta <- data.frame(
118		#' rsp = sample(c(TRUE, FALSE), 100, TRUE),
119		#' grp = factor(rep(c("A", "B"), each = 50)),
120		#' strata = factor(rep(c("V", "W", "X", "Y", "Z"), each = 20))
121		#' )
122		#'
123		#' # With `rtables` pipelines.
124		#' l <- basic_table() %>%
125		#' split_cols_by(var = "grp", ref_group = "B") %>%
126		#' test_proportion_diff(
127		#' vars = "rsp",
128		#' method = "cmh", variables = list(strata = "strata")
129		#' )
130		#'
131		#' build_table(l, df = dta)
132		#'
133		#' @export
134		#' @order 2
135		test_proportion_diff <- function(lyt,
136		vars,
137		variables = list(strata = NULL),
138		method = c("chisq", "schouten", "fisher", "cmh"),
139		na_str = default_na_str(),
140		nested = TRUE,
141		...,
142		var_labels = vars,
143		show_labels = "hidden",
144		table_names = vars,
145		.stats = NULL,
146		.formats = NULL,
147		.labels = NULL,
148		.indent_mods = NULL) {
149	6x	extra_args <- list(variables = variables, method = method, ...)
150
151	6x	afun <- make_afun(
152	6x	a_test_proportion_diff,
153	6x	.stats = .stats,
154	6x	.formats = .formats,
155	6x	.labels = .labels,
156	6x	.indent_mods = .indent_mods
157		)
158	6x	analyze(
159	6x	lyt,
160	6x	vars,
161	6x	afun = afun,
162	6x	var_labels = var_labels,
163	6x	na_str = na_str,
164	6x	nested = nested,
165	6x	extra_args = extra_args,
166	6x	show_labels = show_labels,
167	6x	table_names = table_names
168		)
169		}
170
171		#' Helper Functions to Test Proportion Differences
172		#'
173		#' Helper functions to implement various tests on the difference between two proportions.
174		#'
175		#' @param tbl (`matrix`)\cr matrix with two groups in rows and the binary response (`TRUE`/`FALSE`) in columns.
176		#'
177		#' @return A p-value.
178		#'
179		#' @seealso [prop_diff_test()] for implementation of these helper functions.
180		#'
181		#' @name h_prop_diff_test
182		NULL
183
184		#' @describeIn h_prop_diff_test performs Chi-Squared test. Internally calls [stats::prop.test()].
185		#'
186		#' @keywords internal
187		prop_chisq <- function(tbl) {
188	30x	checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
189	30x	tbl <- tbl[, c("TRUE", "FALSE")]
190	30x	if (any(colSums(tbl) == 0)) {
191	2x	return(1)
192		}
193	28x	stats::prop.test(tbl, correct = FALSE)$p.value
194		}
195
196		#' @describeIn h_prop_diff_test performs stratified Cochran-Mantel-Haenszel test. Internally calls
197		#' [stats::mantelhaen.test()]. Note that strata with less than two observations are automatically discarded.
198		#'
199		#' @param ary (`array`, 3 dimensions)\cr array with two groups in rows, the binary response
200		#' (`TRUE`/`FALSE`) in columns, and the strata in the third dimension.
201		#'
202		#' @keywords internal
203		prop_cmh <- function(ary) {
204	16x	checkmate::assert_array(ary)
205	16x	checkmate::assert_integer(c(ncol(ary), nrow(ary)), lower = 2, upper = 2)
206	16x	checkmate::assert_integer(length(dim(ary)), lower = 3, upper = 3)
207	16x	strata_sizes <- apply(ary, MARGIN = 3, sum)
208	16x	if (any(strata_sizes < 5)) {
209	1x	warning("<5 data points in some strata. CMH test may be incorrect.")
210	1x	ary <- ary[, , strata_sizes > 1]
211		}
212
213	16x	stats::mantelhaen.test(ary, correct = FALSE)$p.value
214		}
215
216		#' @describeIn h_prop_diff_test performs the Chi-Squared test with Schouten correction.
217		#'
218		#' @seealso Schouten correction is based upon \insertCite{Schouten1980-kd;textual}{tern}.
219		#'
220		#' @keywords internal
221		prop_schouten <- function(tbl) {
222	100x	checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
223	100x	tbl <- tbl[, c("TRUE", "FALSE")]
224	100x	if (any(colSums(tbl) == 0)) {
225	1x	return(1)
226		}
227
228	99x	n <- sum(tbl)
229	99x	n1 <- sum(tbl[1, ])
230	99x	n2 <- sum(tbl[2, ])
231
232	99x	ad <- diag(tbl)
233	99x	bc <- diag(apply(tbl, 2, rev))
234	99x	ac <- tbl[, 1]
235	99x	bd <- tbl[, 2]
236
237	99x	t_schouten <- (n - 1) *
238	99x	(abs(prod(ad) - prod(bc)) - 0.5 * min(n1, n2))^2 /
239	99x	(n1 * n2 * sum(ac) * sum(bd))
240
241	99x	1 - stats::pchisq(t_schouten, df = 1)
242		}
243
244		#' @describeIn h_prop_diff_test performs the Fisher's exact test. Internally calls [stats::fisher.test()].
245		#'
246		#' @keywords internal
247		prop_fisher <- function(tbl) {
248	2x	checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
249	2x	tbl <- tbl[, c("TRUE", "FALSE")]
250	2x	stats::fisher.test(tbl)$p.value
251		}

1		#' Custom Split Functions
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Collection of useful functions that are expanding on the core list of functions
6		#' provided by `rtables`. See [rtables::custom_split_funs] and [rtables::make_split_fun()]
7		#' for more information on how to make a custom split function. All these functions
8		#' work with [split_rows_by()] argument `split_fun` to modify the way the split
9		#' happens. For other split functions, consider consulting [`rtables::split_funcs`].
10		#'
11		#' @seealso [rtables::make_split_fun()]
12		#'
13		#' @name utils_split_funs
14		NULL
15
16		#' @describeIn utils_split_funs split function to place reference group facet at a specific position
17		#' during post-processing stage.
18		#'
19		#' @param position (`string` or `integer`)\cr should it be `"first"` or `"last"` or in a specific position?
20		#'
21		#' @return
22		#' * `ref_group_position` returns an utility function that puts the reference group
23		#' as first, last or at a certain position and needs to be assigned to `split_fun`.
24		#'
25		#' @examples
26		#' library(dplyr)
27		#'
28		#' dat <- data.frame(
29		#' x = factor(letters[1:5], levels = letters[5:1]),
30		#' y = 1:5
31		#' )
32		#'
33		#' # With rtables layout functions
34		#' basic_table() %>%
35		#' split_cols_by("x", ref_group = "c", split_fun = ref_group_position("last")) %>%
36		#' analyze("y") %>%
37		#' build_table(dat)
38		#'
39		#' # With tern layout funcitons
40		#' adtte_f <- tern_ex_adtte %>%
41		#' filter(PARAMCD == "OS") %>%
42		#' mutate(
43		#' AVAL = day2month(AVAL),
44		#' is_event = CNSR == 0
45		#' )
46		#'
47		#' basic_table() %>%
48		#' split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position("first")) %>%
49		#' add_colcounts() %>%
50		#' surv_time(
51		#' vars = "AVAL",
52		#' var_labels = "Survival Time (Months)",
53		#' is_event = "is_event",
54		#' ) %>%
55		#' build_table(df = adtte_f)
56		#'
57		#' basic_table() %>%
58		#' split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position(2)) %>%
59		#' add_colcounts() %>%
60		#' surv_time(
61		#' vars = "AVAL",
62		#' var_labels = "Survival Time (Months)",
63		#' is_event = "is_event",
64		#' ) %>%
65		#' build_table(df = adtte_f)
66		#'
67		#' @export
68		ref_group_position <- function(position = "first") {
69	20x	make_split_fun(
70	20x	post = list(
71	20x	function(splret, spl, fulldf) {
72	57x	if (!"ref_group_value" %in% methods::slotNames(spl)) {
73	1x	stop("Reference group is undefined.")
74		}
75
76	56x	spl_var <- rtables:::spl_payload(spl)
77	56x	fulldf[[spl_var]] <- factor(fulldf[[spl_var]])
78	56x	init_lvls <- levels(fulldf[[spl_var]])
79
80	56x	if (!all(names(splret$values) %in% init_lvls)) {
81	!	stop("This split function does not work with combination facets.")
82		}
83
84	56x	ref_group_pos <- which(init_lvls == rtables:::spl_ref_group(spl))
85	56x	pos_choices <- c("first", "last")
86	56x	if (checkmate::test_choice(position, pos_choices) && position == "first") {
87	41x	pos <- 0
88	15x	} else if (checkmate::test_choice(position, pos_choices) && position == "last") {
89	12x	pos <- length(init_lvls)
90	3x	} else if (checkmate::test_int(position, lower = 1, upper = length(init_lvls))) {
91	3x	pos <- position - 1
92		} else {
93	!	stop("Wrong input for ref group position. It must be 'first', 'last', or a integer.")
94		}
95
96	56x	reord_lvls <- append(init_lvls[-ref_group_pos], init_lvls[ref_group_pos], after = pos)
97	56x	ord <- match(reord_lvls, names(splret$values))
98
99	56x	make_split_result(
100	56x	splret$values[ord],
101	56x	splret$datasplit[ord],
102	56x	splret$labels[ord]
103		)
104		}
105		)
106		)
107		}
108
109		#' @describeIn utils_split_funs split function to change level order based on a `integer`
110		#' vector or a `character` vector that represent the split variable's factor levels.
111		#'
112		#' @param order (`character` or `integer`)\cr vector of ordering indexes for the split facets.
113		#'
114		#' @return
115		#' * `level_order` returns an utility function that changes the original levels' order,
116		#' depending on input `order` and split levels.
117		#'
118		#' @examples
119		#' # level_order --------
120		#' # Even if default would bring ref_group first, the original order puts it last
121		#' basic_table() %>%
122		#' split_cols_by("Species", split_fun = level_order(c(1, 3, 2))) %>%
123		#' analyze("Sepal.Length") %>%
124		#' build_table(iris)
125		#'
126		#' # character vector
127		#' new_order <- level_order(levels(iris$Species)[c(1, 3, 2)])
128		#' basic_table() %>%
129		#' split_cols_by("Species", ref_group = "virginica", split_fun = new_order) %>%
130		#' analyze("Sepal.Length") %>%
131		#' build_table(iris)
132		#'
133		#' @export
134		level_order <- function(order) {
135	2x	make_split_fun(
136	2x	post = list(
137	2x	function(splret, spl, fulldf) {
138	4x	if (checkmate::test_integerish(order)) {
139	1x	checkmate::assert_integerish(order, lower = 1, upper = length(splret$values))
140	1x	ord <- order
141		} else {
142	3x	checkmate::assert_character(order, len = length(splret$values))
143	3x	checkmate::assert_set_equal(order, names(splret$values), ordered = FALSE)
144	3x	ord <- match(order, names(splret$values))
145		}
146	4x	make_split_result(
147	4x	splret$values[ord],
148	4x	splret$datasplit[ord],
149	4x	splret$labels[ord]
150		)
151		}
152		)
153		)
154		}

1		#' Survival Time Analysis
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Summarize median survival time and CIs, percentiles of survival times, survival
6		#' time range of censored/event patients.
7		#'
8		#' @inheritParams argument_convention
9		#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function
10		#' [control_surv_time()]. Some possible parameter options are:
11		#' * `conf_level` (`proportion`)\cr confidence level of the interval for survival time.
12		#' * `conf_type` (`string`)\cr confidence interval type. Options are "plain" (default), "log", or "log-log",
13		#' see more in [survival::survfit()]. Note option "none" is not supported.
14		#' * `quantiles` (`numeric`)\cr vector of length two to specify the quantiles of survival time.
15		#' @param ref_fn_censor (`flag`)\cr whether referential footnotes indicating censored observations should be printed
16		#' when the `range` statistic is included.
17		#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("surv_time")`
18		#' to see available statistics for this function.
19		#' @param .indent_mods (named `vector` of `integer`)\cr indent modifiers for the labels. Each element of the vector
20		#' should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
21		#' for that statistic's row label.
22		#'
23		#' @examples
24		#' library(dplyr)
25		#'
26		#' adtte_f <- tern_ex_adtte %>%
27		#' filter(PARAMCD == "OS") %>%
28		#' mutate(
29		#' AVAL = day2month(AVAL),
30		#' is_event = CNSR == 0
31		#' )
32		#' df <- adtte_f %>% filter(ARMCD == "ARM A")
33		#'
34		#' @name survival_time
35		#' @order 1
36		NULL
37
38		#' @describeIn survival_time Statistics function which analyzes survival times.
39		#'
40		#' @return
41		#' * `s_surv_time()` returns the statistics:
42		#' * `median`: Median survival time.
43		#' * `median_ci`: Confidence interval for median time.
44		#' * `quantiles`: Survival time for two specified quantiles.
45		#' * `range_censor`: Survival time range for censored observations.
46		#' * `range_event`: Survival time range for observations with events.
47		#' * `range`: Survival time range for all observations.
48		#'
49		#' @keywords internal
50		s_surv_time <- function(df,
51		.var,
52		is_event,
53		control = control_surv_time()) {
54	182x	checkmate::assert_string(.var)
55	182x	assert_df_with_variables(df, list(tte = .var, is_event = is_event))
56	182x	checkmate::assert_numeric(df[[.var]], min.len = 1, any.missing = FALSE)
57	182x	checkmate::assert_logical(df[[is_event]], min.len = 1, any.missing = FALSE)
58
59	182x	conf_type <- control$conf_type
60	182x	conf_level <- control$conf_level
61	182x	quantiles <- control$quantiles
62
63	182x	formula <- stats::as.formula(paste0("survival::Surv(", .var, ", ", is_event, ") ~ 1"))
64	182x	srv_fit <- survival::survfit(
65	182x	formula = formula,
66	182x	data = df,
67	182x	conf.int = conf_level,
68	182x	conf.type = conf_type
69		)
70	182x	srv_tab <- summary(srv_fit, extend = TRUE)$table
71	182x	srv_qt_tab <- stats::quantile(srv_fit, probs = quantiles)$quantile
72	182x	range_censor <- range_noinf(df[[.var]][!df[[is_event]]], na.rm = TRUE)
73	182x	range_event <- range_noinf(df[[.var]][df[[is_event]]], na.rm = TRUE)
74	182x	range <- range_noinf(df[[.var]], na.rm = TRUE)
75	182x	list(
76	182x	median = formatters::with_label(unname(srv_tab["median"]), "Median"),
77	182x	median_ci = formatters::with_label(
78	182x	unname(srv_tab[paste0(srv_fit$conf.int, c("LCL", "UCL"))]), f_conf_level(conf_level)
79		),
80	182x	quantiles = formatters::with_label(
81	182x	unname(srv_qt_tab), paste0(quantiles[1] * 100, "% and ", quantiles[2] * 100, "%-ile")
82		),
83	182x	range_censor = formatters::with_label(range_censor, "Range (censored)"),
84	182x	range_event = formatters::with_label(range_event, "Range (event)"),
85	182x	range = formatters::with_label(range, "Range")
86		)
87		}
88
89		#' @describeIn survival_time Formatted analysis function which is used as `afun` in `surv_time()`.
90		#'
91		#' @return
92		#' * `a_surv_time()` returns the corresponding list with formatted [rtables::CellValue()].
93		#'
94		#' @examples
95		#' a_surv_time(
96		#' df,
97		#' .df_row = df,
98		#' .var = "AVAL",
99		#' is_event = "is_event"
100		#' )
101		#'
102		#' @export
103		a_surv_time <- function(df,
104		labelstr = "",
105		.var = NULL,
106		.df_row = NULL,
107		is_event,
108		control = control_surv_time(),
109		ref_fn_censor = TRUE,
110		.stats = NULL,
111		.formats = NULL,
112		.labels = NULL,
113		.indent_mods = NULL,
114		na_str = default_na_str()) {
115	12x	x_stats <- s_surv_time(
116	12x	df = df, .var = .var, is_event = is_event, control = control
117		)
118	12x	rng_censor_lwr <- x_stats[["range_censor"]][1]
119	12x	rng_censor_upr <- x_stats[["range_censor"]][2]
120
121		# Use method-specific defaults
122	12x	fmts <- c(median_ci = "(xx.x, xx.x)", quantiles = "xx.x, xx.x", range = "xx.x to xx.x")
123	12x	lbls <- c(median_ci = "95% CI", range = "Range", range_censor = "Range (censored)", range_event = "Range (event)")
124	12x	lbls_custom <- .labels
125	12x	.formats <- c(.formats, fmts[setdiff(names(fmts), names(.formats))])
126	12x	.labels <- c(.labels, lbls[setdiff(names(lbls), names(lbls_custom))])
127
128		# Fill in with formatting defaults if needed
129	12x	.stats <- get_stats("surv_time", stats_in = .stats)
130	12x	.formats <- get_formats_from_stats(.stats, .formats)
131	12x	.labels <- get_labels_from_stats(.stats, .labels) %>% labels_use_control(control, lbls_custom)
132	12x	.indent_mods <- get_indents_from_stats(.stats, .indent_mods)
133
134	12x	x_stats <- x_stats[.stats]
135
136		# Auto format handling
137	12x	.formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)
138
139	12x	cell_fns <- setNames(vector("list", length = length(x_stats)), .labels)
140	12x	if ("range" %in% names(x_stats) && ref_fn_censor) {
141	12x	if (x_stats[["range"]][1] == rng_censor_lwr && x_stats[["range"]][2] == rng_censor_upr) {
142	1x	cell_fns[[.labels[["range"]]]] <- "Censored observations: range minimum & maximum"
143	11x	} else if (x_stats[["range"]][1] == rng_censor_lwr) {
144	2x	cell_fns[[.labels[["range"]]]] <- "Censored observation: range minimum"
145	9x	} else if (x_stats[["range"]][2] == rng_censor_upr) {
146	1x	cell_fns[[.labels[["range"]]]] <- "Censored observation: range maximum"
147		}
148		}
149
150	12x	in_rows(
151	12x	.list = x_stats,
152	12x	.formats = .formats,
153	12x	.names = .labels,
154	12x	.labels = .labels,
155	12x	.indent_mods = .indent_mods,
156	12x	.format_na_strs = na_str,
157	12x	.cell_footnotes = cell_fns
158		)
159		}
160
161		#' @describeIn survival_time Layout-creating function which can take statistics function arguments
162		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
163		#'
164		#' @return
165		#' * `surv_time()` returns a layout object suitable for passing to further layouting functions,
166		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
167		#' the statistics from `s_surv_time()` to the table layout.
168		#'
169		#' @examples
170		#' basic_table() %>%
171		#' split_cols_by(var = "ARMCD") %>%
172		#' add_colcounts() %>%
173		#' surv_time(
174		#' vars = "AVAL",
175		#' var_labels = "Survival Time (Months)",
176		#' is_event = "is_event",
177		#' control = control_surv_time(conf_level = 0.9, conf_type = "log-log")
178		#' ) %>%
179		#' build_table(df = adtte_f)
180		#'
181		#' @export
182		#' @order 2
183		surv_time <- function(lyt,
184		vars,
185		is_event,
186		control = control_surv_time(),
187		ref_fn_censor = TRUE,
188		na_str = default_na_str(),
189		nested = TRUE,
190		...,
191		var_labels = "Time to Event",
192		show_labels = "visible",
193		table_names = vars,
194		.stats = c("median", "median_ci", "quantiles", "range"),
195		.formats = NULL,
196		.labels = NULL,
197		.indent_mods = c(median_ci = 1L)) {
198	3x	extra_args <- list(
199	3x	.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str,
200	3x	is_event = is_event, control = control, ref_fn_censor = ref_fn_censor, ...
201		)
202
203	3x	analyze(
204	3x	lyt = lyt,
205	3x	vars = vars,
206	3x	afun = a_surv_time,
207	3x	var_labels = var_labels,
208	3x	show_labels = show_labels,
209	3x	table_names = table_names,
210	3x	na_str = na_str,
211	3x	nested = nested,
212	3x	extra_args = extra_args
213		)
214		}

1		#' Split Function to Configure Risk Difference Column
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Wrapper function for [rtables::add_combo_levels()] which configures settings for the risk difference
6		#' column to be added to an `rtables` object. To add a risk difference column to a table, this function
7		#' should be used as `split_fun` in calls to [rtables::split_cols_by()], followed by setting argument
8		#' `riskdiff` to `TRUE` in all following analyze function calls.
9		#'
10		#' @param arm_x (`character`)\cr Name of reference arm to use in risk difference calculations.
11		#' @param arm_y (`character`)\cr Names of one or more arms to compare to reference arm in risk difference
12		#' calculations. A new column will be added for each value of `arm_y`.
13		#' @param col_label (`character`)\cr Labels to use when rendering the risk difference column within the table.
14		#' If more than one comparison arm is specified in `arm_y`, default labels will specify which two arms are
15		#' being compared (reference arm vs. comparison arm).
16		#' @param pct (`flag`)\cr whether output should be returned as percentages. Defaults to `TRUE`.
17		#'
18		#' @return A closure suitable for use as a split function (`split_fun`) within [rtables::split_cols_by()]
19		#' when creating a table layout.
20		#'
21		#' @seealso [stat_propdiff_ci()] for details on risk difference calculation.
22		#'
23		#' @examples
24		#' adae <- tern_ex_adae
25		#' adae$AESEV <- factor(adae$AESEV)
26		#'
27		#' lyt <- basic_table() %>%
28		#' split_cols_by("ARMCD", split_fun = add_riskdiff(arm_x = "ARM A", arm_y = c("ARM B", "ARM C"))) %>%
29		#' count_occurrences_by_grade(
30		#' var = "AESEV",
31		#' riskdiff = TRUE
32		#' )
33		#'
34		#' tbl <- build_table(lyt, df = adae)
35		#' tbl
36		#'
37		#' @export
38		add_riskdiff <- function(arm_x,
39		arm_y,
40		col_label = paste0(
41		"Risk Difference (%) (95% CI)", if (length(arm_y) > 1) paste0("\n", arm_x, " vs. ", arm_y)
42		),
43		pct = TRUE) {
44	7x	checkmate::assert_character(arm_x, len = 1)
45	7x	checkmate::assert_character(arm_y, min.len = 1)
46	7x	checkmate::assert_character(col_label, len = length(arm_y))
47
48	7x	combodf <- tibble::tribble(~valname, ~label, ~levelcombo, ~exargs)
49	7x	for (i in seq_len(length(arm_y))) {
50	7x	combodf <- rbind(
51	7x	combodf,
52	7x	tibble::tribble(
53	7x	~valname, ~label, ~levelcombo, ~exargs,
54	7x	paste("riskdiff", arm_x, arm_y[i], sep = "_"), col_label[i], c(arm_x, arm_y[i]), list()
55		)
56		)
57		}
58	7x	if (pct) combodf$valname <- paste0(combodf$valname, "_pct")
59	7x	add_combo_levels(combodf)
60		}
61
62		#' Analysis Function to Calculate Risk Difference Column Values
63		#'
64		#' In the risk difference column, this function uses the statistics function associated with `afun` to
65		#' calculates risk difference values from arm X (reference group) and arm Y. These arms are specified
66		#' when configuring the risk difference column which is done using the [add_riskdiff()] split function in
67		#' the previous call to [rtables::split_cols_by()]. For all other columns, applies `afun` as usual. This
68		#' function utilizes the [stat_propdiff_ci()] function to perform risk difference calculations.
69		#'
70		#' @inheritParams argument_convention
71		#' @param afun (named `list`)\cr A named list containing one name-value pair where the name corresponds to
72		#' the name of the statistics function that should be used in calculations and the value is the corresponding
73		#' analysis function.
74		#' @param s_args (named `list`)\cr Additional arguments to be passed to the statistics function and analysis
75		#' function supplied in `afun`.
76		#'
77		#' @return A list of formatted [rtables::CellValue()].
78		#'
79		#' @seealso
80		#' * [stat_propdiff_ci()] for details on risk difference calculation.
81		#' * Split function [add_riskdiff()] which, when used as `split_fun` within [rtables::split_cols_by()] with
82		#' `riskdiff` argument set to `TRUE` in subsequent analyze functions calls, adds a risk difference column
83		#' to a table layout.
84		#'
85		#' @keywords internal
86		afun_riskdiff <- function(df,
87		labelstr = "",
88		.var,
89		.N_col, # nolint
90		.N_row, # nolint
91		.df_row,
92		.spl_context,
93		.all_col_counts,
94		.stats,
95		.formats = NULL,
96		.labels = NULL,
97		.indent_mods = NULL,
98		na_str = default_na_str(),
99		afun,
100		s_args = list()) {
101	44x	if (!any(grepl("riskdiff", names(.spl_context)))) {
102	!	stop(
103	!	"Please set up levels to use in risk difference calculations using the `add_riskdiff` ",
104	!	"split function within `split_cols_by`. See ?add_riskdiff for details."
105		)
106		}
107	44x	checkmate::assert_list(afun, len = 1, types = "function")
108	44x	checkmate::assert_named(afun)
109	44x	afun_args <- list(
110	44x	.var = .var, .df_row = .df_row, .N_row = .N_row, denom = "N_col", labelstr = labelstr,
111	44x	.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
112		)
113	44x	afun_args <- afun_args[intersect(names(afun_args), names(as.list(args(afun[[1]]))))]
114	!	if ("denom" %in% names(s_args)) afun_args[["denom"]] <- NULL
115
116	44x	cur_split <- tail(.spl_context$cur_col_split_val[[1]], 1)
117	44x	if (!grepl("^riskdiff", cur_split)) {
118		# Apply basic afun (no risk difference) in all other columns
119	33x	do.call(afun[[1]], args = c(list(df = df, .N_col = .N_col), afun_args, s_args))
120		} else {
121	11x	arm_x <- strsplit(cur_split, "_")[[1]][2]
122	11x	arm_y <- strsplit(cur_split, "_")[[1]][3]
123	11x	if (length(.spl_context$cur_col_split[[1]]) > 1) { # Different split name for nested column splits
124	!	arm_spl_x <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 2)], collapse = ""))
125	!	arm_spl_y <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 3)], collapse = ""))
126		} else {
127	11x	arm_spl_x <- arm_x
128	11x	arm_spl_y <- arm_y
129		}
130	11x	N_col_x <- .all_col_counts[[arm_spl_x]] # nolint
131	11x	N_col_y <- .all_col_counts[[arm_spl_y]] # nolint
132	11x	cur_var <- tail(.spl_context$cur_col_split[[1]], 1)
133
134		# Apply statistics function to arm X and arm Y data
135	11x	s_args <- c(s_args, afun_args[intersect(names(afun_args), names(as.list(args(names(afun)))))])
136	11x	s_x <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_x, ], .N_col = N_col_x), s_args))
137	11x	s_y <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_y, ], .N_col = N_col_y), s_args))
138
139		# Get statistic name and row names
140	11x	stat <- ifelse("count_fraction" %in% names(s_x), "count_fraction", "unique")
141	11x	if ("flag_variables" %in% names(s_args)) {
142	1x	var_nms <- s_args$flag_variables
143	10x	} else if (!is.null(names(s_x[[stat]]))) {
144	4x	var_nms <- names(s_x[[stat]])
145		} else {
146	6x	var_nms <- ""
147	6x	s_x[[stat]] <- list(s_x[[stat]])
148	6x	s_y[[stat]] <- list(s_y[[stat]])
149		}
150
151		# Calculate risk difference for each row, repeated if multiple statistics in table
152	11x	pct <- tail(strsplit(cur_split, "_")[[1]], 1) == "pct"
153	11x	rd_ci <- rep(stat_propdiff_ci(
154	11x	lapply(s_x[[stat]], `[`, 1), lapply(s_y[[stat]], `[`, 1),
155	11x	N_col_x, N_col_y,
156	11x	list_names = var_nms,
157	11x	pct = pct
158	11x	), max(1, length(.stats)))
159
160	11x	in_rows(.list = rd_ci, .formats = "xx.x (xx.x - xx.x)", .indent_mods = .indent_mods)
161		}
162		}

1		#' Tabulate Binary Response by Subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Tabulate statistics such as response rate and odds ratio for population subgroups.
6		#'
7		#' @inheritParams extract_rsp_subgroups
8		#' @inheritParams argument_convention
9		#'
10		#' @details These functions create a layout starting from a data frame which contains
11		#' the required statistics. Tables typically used as part of forest plot.
12		#'
13		#' @seealso [extract_rsp_subgroups()]
14		#'
15		#' @examples
16		#' library(dplyr)
17		#' library(forcats)
18		#'
19		#' adrs <- tern_ex_adrs
20		#' adrs_labels <- formatters::var_labels(adrs)
21		#'
22		#' adrs_f <- adrs %>%
23		#' filter(PARAMCD == "BESRSPI") %>%
24		#' filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
25		#' droplevels() %>%
26		#' mutate(
27		#' # Reorder levels of factor to make the placebo group the reference arm.
28		#' ARM = fct_relevel(ARM, "B: Placebo"),
29		#' rsp = AVALC == "CR"
30		#' )
31		#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
32		#'
33		#' # Unstratified analysis.
34		#' df <- extract_rsp_subgroups(
35		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
36		#' data = adrs_f
37		#' )
38		#' df
39		#'
40		#' # Stratified analysis.
41		#' df_strat <- extract_rsp_subgroups(
42		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2"), strata = "STRATA1"),
43		#' data = adrs_f
44		#' )
45		#' df_strat
46		#'
47		#' # Grouping of the BMRKR2 levels.
48		#' df_grouped <- extract_rsp_subgroups(
49		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
50		#' data = adrs_f,
51		#' groups_lists = list(
52		#' BMRKR2 = list(
53		#' "low" = "LOW",
54		#' "low/medium" = c("LOW", "MEDIUM"),
55		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
56		#' )
57		#' )
58		#' )
59		#' df_grouped
60		#'
61		#' @name response_subgroups
62		#' @order 1
63		NULL
64
65		#' Prepares Response Data for Population Subgroups in Data Frames
66		#'
67		#' @description `r lifecycle::badge("stable")`
68		#'
69		#' Prepares response rates and odds ratios for population subgroups in data frames. Simple wrapper
70		#' for [h_odds_ratio_subgroups_df()] and [h_proportion_subgroups_df()]. Result is a list of two
71		#' `data.frames`: `prop` and `or`. `variables` corresponds to the names of variables found in `data`,
72		#' passed as a named `list` and requires elements `rsp`, `arm` and optionally `subgroups` and `strata`.
73		#' `groups_lists` optionally specifies groupings for `subgroups` variables.
74		#'
75		#' @inheritParams argument_convention
76		#' @inheritParams response_subgroups
77		#' @param label_all (`string`)\cr label for the total population analysis.
78		#'
79		#' @return A named list of two elements:
80		#' * `prop`: A `data.frame` containing columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`, `var`,
81		#' `var_label`, and `row_type`.
82		#' * `or`: A `data.frame` containing columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`,
83		#' `subgroup`, `var`, `var_label`, and `row_type`.
84		#'
85		#' @seealso [response_subgroups]
86		#'
87		#' @export
88		extract_rsp_subgroups <- function(variables,
89		data,
90		groups_lists = list(),
91		conf_level = 0.95,
92		method = NULL,
93		label_all = "All Patients") {
94	11x	if ("strat" %in% names(variables)) {
95	!	warning(
96	!	"Warning: the `strat` element name of the `variables` list argument to `extract_rsp_subgroups() ",
97	!	"was deprecated in tern 0.9.3.\n ",
98	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
99		)
100	!	variables[["strata"]] <- variables[["strat"]]
101		}
102
103	11x	df_prop <- h_proportion_subgroups_df(
104	11x	variables,
105	11x	data,
106	11x	groups_lists = groups_lists,
107	11x	label_all = label_all
108		)
109	11x	df_or <- h_odds_ratio_subgroups_df(
110	11x	variables,
111	11x	data,
112	11x	groups_lists = groups_lists,
113	11x	conf_level = conf_level,
114	11x	method = method,
115	11x	label_all = label_all
116		)
117
118	11x	list(prop = df_prop, or = df_or)
119		}
120
121		#' @describeIn response_subgroups Formatted analysis function which is used as `afun` in `tabulate_rsp_subgroups()`.
122		#'
123		#' @return
124		#' * `a_response_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
125		#'
126		#' @keywords internal
127		a_response_subgroups <- function(.formats = list(
128		n = "xx", # nolint start
129		n_rsp = "xx",
130		prop = "xx.x%",
131		n_tot = "xx",
132		or = list(format_extreme_values(2L)),
133		ci = list(format_extreme_values_ci(2L)),
134		pval = "x.xxxx \| (<0.0001)" # nolint end
135		),
136		na_str = default_na_str()) {
137	17x	checkmate::assert_list(.formats)
138	17x	checkmate::assert_subset(
139	17x	names(.formats),
140	17x	c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval")
141		)
142
143	17x	afun_lst <- Map(
144	17x	function(stat, fmt, na_str) {
145	114x	if (stat == "ci") {
146	16x	function(df, labelstr = "", ...) {
147	36x	in_rows(
148	36x	.list = combine_vectors(df$lcl, df$ucl),
149	36x	.labels = as.character(df$subgroup),
150	36x	.formats = fmt,
151	36x	.format_na_strs = na_str
152		)
153		}
154		} else {
155	98x	function(df, labelstr = "", ...) {
156	211x	in_rows(
157	211x	.list = as.list(df[[stat]]),
158	211x	.labels = as.character(df$subgroup),
159	211x	.formats = fmt,
160	211x	.format_na_strs = na_str
161		)
162		}
163		}
164		},
165	17x	stat = names(.formats),
166	17x	fmt = .formats,
167	17x	na_str = na_str
168		)
169
170	17x	afun_lst
171		}
172
173		#' @describeIn response_subgroups Table-creating function which creates a table
174		#' summarizing binary response by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
175		#' and [rtables::summarize_row_groups()].
176		#'
177		#' @param df (`list`)\cr of data frames containing all analysis variables. List should be
178		#' created using [extract_rsp_subgroups()].
179		#' @param vars (`character`)\cr the names of statistics to be reported among:
180		#' * `n`: Total number of observations per group.
181		#' * `n_rsp`: Number of responders per group.
182		#' * `prop`: Proportion of responders.
183		#' * `n_tot`: Total number of observations.
184		#' * `or`: Odds ratio.
185		#' * `ci` : Confidence interval of odds ratio.
186		#' * `pval`: p-value of the effect.
187		#' Note, the statistics `n_tot`, `or` and `ci` are required.
188		#'
189		#' @return An `rtables` table summarizing binary response by subgroup.
190		#'
191		#' @examples
192		#' ## Table with default columns.
193		#' basic_table() %>%
194		#' tabulate_rsp_subgroups(df)
195		#'
196		#' ## Table with selected columns.
197		#' basic_table() %>%
198		#' tabulate_rsp_subgroups(
199		#' df = df,
200		#' vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci")
201		#' )
202		#'
203		#' @export
204		#' @order 2
205		tabulate_rsp_subgroups <- function(lyt,
206		df,
207		vars = c("n_tot", "n", "prop", "or", "ci"),
208		groups_lists = list(),
209		label_all = "All Patients",
210		na_str = default_na_str()) {
211	8x	conf_level <- df$or$conf_level[1]
212	8x	method <- if ("pval_label" %in% names(df$or)) {
213	5x	df$or$pval_label[1]
214		} else {
215	3x	NULL
216		}
217
218	8x	extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method, label_all = label_all)
219
220	8x	afun_lst <- a_response_subgroups(na_str = na_str)
221	8x	colvars <- d_rsp_subgroups_colvars(vars, conf_level = conf_level, method = method)
222
223	8x	colvars_prop <- list(
224	8x	vars = colvars$vars[names(colvars$labels) %in% c("n", "prop", "n_rsp")],
225	8x	labels = colvars$labels[names(colvars$labels) %in% c("n", "prop", "n_rsp")]
226		)
227	8x	colvars_or <- list(
228	8x	vars = colvars$vars[names(colvars$labels) %in% c("n_tot", "or", "ci", "pval")],
229	8x	labels = colvars$labels[names(colvars$labels) %in% c("n_tot", "or", "ci", "pval")]
230		)
231
232		# Columns from table_prop are optional.
233	8x	if (length(colvars_prop$vars) > 0) {
234	8x	lyt_prop <- split_cols_by(lyt = lyt, var = "arm")
235	8x	lyt_prop <- split_cols_by_multivar(
236	8x	lyt = lyt_prop,
237	8x	vars = colvars_prop$vars,
238	8x	varlabels = colvars_prop$labels
239		)
240
241		# "All Patients" row
242	8x	lyt_prop <- split_rows_by(
243	8x	lyt = lyt_prop,
244	8x	var = "row_type",
245	8x	split_fun = keep_split_levels("content"),
246	8x	nested = FALSE,
247	8x	child_labels = "hidden"
248		)
249	8x	lyt_prop <- analyze_colvars(
250	8x	lyt = lyt_prop,
251	8x	afun = afun_lst[names(colvars_prop$labels)],
252	8x	na_str = na_str,
253	8x	extra_args = extra_args
254		)
255
256	8x	if ("analysis" %in% df$prop$row_type) {
257	7x	lyt_prop <- split_rows_by(
258	7x	lyt = lyt_prop,
259	7x	var = "row_type",
260	7x	split_fun = keep_split_levels("analysis"),
261	7x	nested = FALSE,
262	7x	child_labels = "hidden"
263		)
264	7x	lyt_prop <- split_rows_by(lyt = lyt_prop, var = "var_label", nested = TRUE)
265	7x	lyt_prop <- analyze_colvars(
266	7x	lyt = lyt_prop,
267	7x	afun = afun_lst[names(colvars_prop$labels)],
268	7x	na_str = na_str,
269	7x	inclNAs = TRUE,
270	7x	extra_args = extra_args
271		)
272		}
273
274	8x	table_prop <- build_table(lyt_prop, df = df$prop)
275		} else {
276	!	table_prop <- NULL
277		}
278
279		# Columns "n_tot", "or", "ci" in table_or are required.
280	8x	lyt_or <- split_cols_by(lyt = lyt, var = "arm")
281	8x	lyt_or <- split_cols_by_multivar(
282	8x	lyt = lyt_or,
283	8x	vars = colvars_or$vars,
284	8x	varlabels = colvars_or$labels
285		)
286
287		# "All Patients" row
288	8x	lyt_or <- split_rows_by(
289	8x	lyt = lyt_or,
290	8x	var = "row_type",
291	8x	split_fun = keep_split_levels("content"),
292	8x	nested = FALSE,
293	8x	child_labels = "hidden"
294		)
295	8x	lyt_or <- analyze_colvars(
296	8x	lyt = lyt_or,
297	8x	afun = afun_lst[names(colvars_or$labels)],
298	8x	na_str = na_str,
299	8x	extra_args = extra_args
300		) %>%
301	8x	append_topleft("Baseline Risk Factors")
302
303	8x	if ("analysis" %in% df$or$row_type) {
304	7x	lyt_or <- split_rows_by(
305	7x	lyt = lyt_or,
306	7x	var = "row_type",
307	7x	split_fun = keep_split_levels("analysis"),
308	7x	nested = FALSE,
309	7x	child_labels = "hidden"
310		)
311	7x	lyt_or <- split_rows_by(lyt = lyt_or, var = "var_label", nested = TRUE)
312	7x	lyt_or <- analyze_colvars(
313	7x	lyt = lyt_or,
314	7x	afun = afun_lst[names(colvars_or$labels)],
315	7x	na_str = na_str,
316	7x	inclNAs = TRUE,
317	7x	extra_args = extra_args
318		)
319		}
320	8x	table_or <- build_table(lyt_or, df = df$or)
321
322	8x	n_tot_id <- match("n_tot", colvars_or$vars)
323	8x	if (is.null(table_prop)) {
324	!	result <- table_or
325	!	or_id <- match("or", colvars_or$vars)
326	!	ci_id <- match("lcl", colvars_or$vars)
327		} else {
328	8x	result <- cbind_rtables(table_or[, n_tot_id], table_prop, table_or[, -n_tot_id])
329	8x	or_id <- 1L + ncol(table_prop) + match("or", colvars_or$vars[-n_tot_id])
330	8x	ci_id <- 1L + ncol(table_prop) + match("lcl", colvars_or$vars[-n_tot_id])
331	8x	n_tot_id <- 1L
332		}
333	8x	structure(
334	8x	result,
335	8x	forest_header = paste0(levels(df$prop$arm), "\nBetter"),
336	8x	col_x = or_id,
337	8x	col_ci = ci_id,
338	8x	col_symbol_size = n_tot_id
339		)
340		}
341
342		#' Labels for Column Variables in Binary Response by Subgroup Table
343		#'
344		#' @description `r lifecycle::badge("stable")`
345		#'
346		#' Internal function to check variables included in [tabulate_rsp_subgroups()] and create column labels.
347		#'
348		#' @inheritParams argument_convention
349		#' @inheritParams tabulate_rsp_subgroups
350		#'
351		#' @return A `list` of variables to tabulate and their labels.
352		#'
353		#' @export
354		d_rsp_subgroups_colvars <- function(vars,
355		conf_level = NULL,
356		method = NULL) {
357	17x	checkmate::assert_character(vars)
358	17x	checkmate::assert_subset(c("n_tot", "or", "ci"), vars)
359	17x	checkmate::assert_subset(
360	17x	vars,
361	17x	c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval")
362		)
363
364	17x	varlabels <- c(
365	17x	n = "n",
366	17x	n_rsp = "Responders",
367	17x	prop = "Response (%)",
368	17x	n_tot = "Total n",
369	17x	or = "Odds Ratio"
370		)
371	17x	colvars <- vars
372
373	17x	if ("ci" %in% colvars) {
374	17x	checkmate::assert_false(is.null(conf_level))
375
376	17x	varlabels <- c(
377	17x	varlabels,
378	17x	ci = paste0(100 * conf_level, "% CI")
379		)
380
381		# The `lcl`` variable is just a placeholder available in the analysis data,
382		# it is not acutally used in the tabulation.
383		# Variables used in the tabulation are lcl and ucl, see `a_response_subgroups` for details.
384	17x	colvars[colvars == "ci"] <- "lcl"
385		}
386
387	17x	if ("pval" %in% colvars) {
388	13x	varlabels <- c(
389	13x	varlabels,
390	13x	pval = method
391		)
392		}
393
394	17x	list(
395	17x	vars = colvars,
396	17x	labels = varlabels[vars]
397		)
398		}

1		#' Create a STEP Graph
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Based on the STEP results, creates a `ggplot` graph showing the estimated HR or OR
6		#' along the continuous biomarker value subgroups.
7		#'
8		#' @param df (`tibble`)\cr result of [tidy.step()].
9		#' @param use_percentile (`flag`)\cr whether to use percentiles for the x axis or actual
10		#' biomarker values.
11		#' @param est (named `list`)\cr `col` and `lty` settings for estimate line.
12		#' @param ci_ribbon (named `list` or `NULL`)\cr `fill` and `alpha` settings for the confidence interval
13		#' ribbon area, or `NULL` to not plot a CI ribbon.
14		#' @param col (`character`)\cr colors.
15		#'
16		#' @return A `ggplot` STEP graph.
17		#'
18		#' @seealso Custom tidy method [tidy.step()].
19		#'
20		#' @examples
21		#' library(nestcolor)
22		#' library(survival)
23		#' lung$sex <- factor(lung$sex)
24		#'
25		#' # Survival example.
26		#' vars <- list(
27		#' time = "time",
28		#' event = "status",
29		#' arm = "sex",
30		#' biomarker = "age"
31		#' )
32		#'
33		#' step_matrix <- fit_survival_step(
34		#' variables = vars,
35		#' data = lung,
36		#' control = c(control_coxph(), control_step(num_points = 10, degree = 2))
37		#' )
38		#' step_data <- broom::tidy(step_matrix)
39		#'
40		#' # Default plot.
41		#' g_step(step_data)
42		#'
43		#' # Add the reference 1 horizontal line.
44		#' library(ggplot2)
45		#' g_step(step_data) +
46		#' ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2)
47		#'
48		#' # Use actual values instead of percentiles, different color for estimate and no CI,
49		#' # use log scale for y axis.
50		#' g_step(
51		#' step_data,
52		#' use_percentile = FALSE,
53		#' est = list(col = "blue", lty = 1),
54		#' ci_ribbon = NULL
55		#' ) + scale_y_log10()
56		#'
57		#' # Adding another curve based on additional column.
58		#' step_data$extra <- exp(step_data$`Percentile Center`)
59		#' g_step(step_data) +
60		#' ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = "green")
61		#'
62		#' # Response example.
63		#' vars <- list(
64		#' response = "status",
65		#' arm = "sex",
66		#' biomarker = "age"
67		#' )
68		#'
69		#' step_matrix <- fit_rsp_step(
70		#' variables = vars,
71		#' data = lung,
72		#' control = c(
73		#' control_logistic(response_definition = "I(response == 2)"),
74		#' control_step()
75		#' )
76		#' )
77		#' step_data <- broom::tidy(step_matrix)
78		#' g_step(step_data)
79		#'
80		#' @export
81		g_step <- function(df,
82		use_percentile = "Percentile Center" %in% names(df),
83		est = list(col = "blue", lty = 1),
84		ci_ribbon = list(fill = getOption("ggplot2.discrete.colour")[1], alpha = 0.5),
85		col = getOption("ggplot2.discrete.colour")) {
86	2x	checkmate::assert_tibble(df)
87	2x	checkmate::assert_flag(use_percentile)
88	2x	checkmate::assert_character(col, null.ok = TRUE)
89	2x	checkmate::assert_list(est, names = "named")
90	2x	checkmate::assert_list(ci_ribbon, names = "named", null.ok = TRUE)
91
92	2x	x_var <- ifelse(use_percentile, "Percentile Center", "Interval Center")
93	2x	df$x <- df[[x_var]]
94	2x	attrs <- attributes(df)
95	2x	df$y <- df[[attrs$estimate]]
96
97		# Set legend names. To be modified also at call level
98	2x	legend_names <- c("Estimate", "CI 95%")
99
100	2x	p <- ggplot2::ggplot(df, ggplot2::aes(x = .data[["x"]], y = .data[["y"]]))
101
102	2x	if (!is.null(col)) {
103	2x	p <- p +
104	2x	ggplot2::scale_color_manual(values = col)
105		}
106
107	2x	if (!is.null(ci_ribbon)) {
108	1x	if (is.null(ci_ribbon$fill)) {
109	!	ci_ribbon$fill <- "lightblue"
110		}
111	1x	p <- p + ggplot2::geom_ribbon(
112	1x	ggplot2::aes(
113	1x	ymin = .data[["ci_lower"]], ymax = .data[["ci_upper"]],
114	1x	fill = legend_names[2]
115		),
116	1x	alpha = ci_ribbon$alpha
117		) +
118	1x	scale_fill_manual(
119	1x	name = "", values = c("CI 95%" = ci_ribbon$fill)
120		)
121		}
122	2x	suppressMessages(p <- p +
123	2x	ggplot2::geom_line(
124	2x	ggplot2::aes(y = .data[["y"]], color = legend_names[1]),
125	2x	linetype = est$lty
126		) +
127	2x	scale_colour_manual(
128	2x	name = "", values = c("Estimate" = "blue")
129		))
130
131	2x	p <- p + ggplot2::labs(x = attrs$biomarker, y = attrs$estimate)
132	2x	if (use_percentile) {
133	1x	p <- p + ggplot2::scale_x_continuous(labels = scales::percent)
134		}
135	2x	p
136		}
137
138		#' Custom Tidy Method for STEP Results
139		#'
140		#' @description `r lifecycle::badge("stable")`
141		#'
142		#' Tidy the STEP results into a `tibble` format ready for plotting.
143		#'
144		#' @param x (`step` matrix)\cr results from [fit_survival_step()].
145		#' @param ... not used here.
146		#'
147		#' @return A `tibble` with one row per STEP subgroup. The estimates and CIs are on the HR or OR scale,
148		#' respectively. Additional attributes carry metadata also used for plotting.
149		#'
150		#' @seealso [g_step()] which consumes the result from this function.
151		#'
152		#' @method tidy step
153		#'
154		#' @examples
155		#' library(survival)
156		#' lung$sex <- factor(lung$sex)
157		#' vars <- list(
158		#' time = "time",
159		#' event = "status",
160		#' arm = "sex",
161		#' biomarker = "age"
162		#' )
163		#' step_matrix <- fit_survival_step(
164		#' variables = vars,
165		#' data = lung,
166		#' control = c(control_coxph(), control_step(num_points = 10, degree = 2))
167		#' )
168		#' broom::tidy(step_matrix)
169		#'
170		#' @export
171		tidy.step <- function(x, ...) { # nolint
172	7x	checkmate::assert_class(x, "step")
173	7x	dat <- as.data.frame(x)
174	7x	nams <- names(dat)
175	7x	is_surv <- "loghr" %in% names(dat)
176	7x	est_var <- ifelse(is_surv, "loghr", "logor")
177	7x	new_est_var <- ifelse(is_surv, "Hazard Ratio", "Odds Ratio")
178	7x	new_y_vars <- c(new_est_var, c("ci_lower", "ci_upper"))
179	7x	names(dat)[match(est_var, nams)] <- new_est_var
180	7x	dat[, new_y_vars] <- exp(dat[, new_y_vars])
181	7x	any_is_na <- any(is.na(dat[, new_y_vars]))
182	7x	any_is_very_large <- any(abs(dat[, new_y_vars]) > 1e10, na.rm = TRUE)
183	7x	if (any_is_na) {
184	2x	warning(paste(
185	2x	"Missing values in the point estimate or CI columns,",
186	2x	"this will lead to holes in the `g_step()` plot"
187		))
188		}
189	7x	if (any_is_very_large) {
190	2x	warning(paste(
191	2x	"Very large absolute values in the point estimate or CI columns,",
192	2x	"consider adding `scale_y_log10()` to the `g_step()` result for plotting"
193		))
194		}
195	7x	if (any_is_na \|\| any_is_very_large) {
196	4x	warning("Consider using larger `bandwidth`, less `num_points` in `control_step()` settings for fitting")
197		}
198	7x	structure(
199	7x	tibble::as_tibble(dat),
200	7x	estimate = new_est_var,
201	7x	biomarker = attr(x, "variables")$biomarker,
202	7x	ci = f_conf_level(attr(x, "control")$conf_level)
203		)
204		}